Heating Systems: Design, Applications and Technology 1536175579, 9781536175578

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ELECTRICAL ENGINEERING DEVELOPMENTS

HEATING SYSTEMS DESIGN, APPLICATIONS AND TECHNOLOGY

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ELECTRICAL ENGINEERING DEVELOPMENTS

HEATING SYSTEMS DESIGN, APPLICATIONS AND TECHNOLOGY

ELIAS MOORE EDITOR

Copyright © 2020 by Nova Science Publishers, Inc. All rights reserved. No part of this book may be reproduced, stored in a retrieval system or transmitted in any form or by any means: electronic, electrostatic, magnetic, tape, mechanical photocopying, recording or otherwise without the written permission of the Publisher. We have partnered with Copyright Clearance Center to make it easy for you to obtain permissions to reuse content from this publication. Simply navigate to this publication’s page on Nova’s website and locate the “Get Permission” button below the title description. This button is linked directly to the title’s permission page on copyright.com. Alternatively, you can visit copyright.com and search by title, ISBN, or ISSN. For further questions about using the service on copyright.com, please contact: Copyright Clearance Center Phone: +1-(978) 750-8400 Fax: +1-(978) 750-4470 E-mail: [email protected].

NOTICE TO THE READER The Publisher has taken reasonable care in the preparation of this book, but makes no expressed or implied warranty of any kind and assumes no responsibility for any errors or omissions. No liability is assumed for incidental or consequential damages in connection with or arising out of information contained in this book. The Publisher shall not be liable for any special, consequential, or exemplary damages resulting, in whole or in part, from the readers’ use of, or reliance upon, this material. Any parts of this book based on government reports are so indicated and copyright is claimed for those parts to the extent applicable to compilations of such works. Independent verification should be sought for any data, advice or recommendations contained in this book. In addition, no responsibility is assumed by the Publisher for any injury and/or damage to persons or property arising from any methods, products, instructions, ideas or otherwise contained in this publication. This publication is designed to provide accurate and authoritative information with regard to the subject matter covered herein. It is sold with the clear understanding that the Publisher is not engaged in rendering legal or any other professional services. If legal or any other expert assistance is required, the services of a competent person should be sought. FROM A DECLARATION OF PARTICIPANTS JOINTLY ADOPTED BY A COMMITTEE OF THE AMERICAN BAR ASSOCIATION AND A COMMITTEE OF PUBLISHERS. Additional color graphics may be available in the e-book version of this book.

Library of Congress Cataloging-in-Publication Data ISBN: 

Published by Nova Science Publishers, Inc. † New York

CONTENTS Preface Chapter 1

Chapter 2

Chapter 3

Chapter 4 Index

vii Decarbonising Heating Systems: The Role of Low Temperature District Heating Kapil Narula

1

Residential Heating System Selection Using MCDM Techniques Yavuz Ozdemir and Sahika Ozdemir

33

Designing a Logistics System to Ensure Efficient Distribution of LPG Energy Yavuz Ozdemir

65

Skin-Systems for Heating Extra-Long Pipelines Michail Strupinskiy and Nikolay Khrenkov

95 113

PREFACE Heating Systems: Design, Applications and Technology first discusses the development of different types of district heating systems, highlighting the main features of low temperature district heating and discussing its potential for supplying decarbonised heat. As buildings consume about 40% of the world’s annual energy consumption globally, the authors focus on the evaluation of residential heating system alternatives using fuzzy numbers. Multi-criteria decision making techniques, fuzzy AHP and fuzzy ANP methods are used for evaluation and the results of both algorithms are compared. Research is presented which is aimed at designing a logistics system for X Gas Company to ensure efficient distribution of liquefied petroleum gas, which begins with the ordering process and ends with the placement of stations in Istanbul-Turkey, taking into account the storage, preparation, loading and delivery operations of X Gas Company. In closing, three types of electro heating skin-systems are presented and the main features of skin heating systems are considered. The advantages of these systems for heating extra-long pipelines transporting oil, gas, water and other liquids are explored. Chapter 1 - It is estimated that heating and cooling contribute to approximately half of the global final energy demand. This is likely to increase in the future due to increase in urbanisation and climate

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conditioned buildings. Boilers using coal, oil and natural gas contribute to the majority of heat supply which results in large GHG emissions. It is therefore essential that heating (and cooling) supply is decarbonised in a carbon constrained world. District heating (DH) system is an effective way of supplying heat in cities having areas with high heat demand density. This chapter examines the role of low temperature district heating (LTDH) in decarbonising heating systems. It presents an overview of the heating demand and supply and emphasises the need for decarbonising heating systems in the background of recent global committments to mitigate climate change. The chapter discusses the development of different types of DH systems. It highlights the main features of a LTDH and discusses its potential for supplying decarbonised heat. The main advantages of LTDH over existing DH systems are explained which include lower heat losses, higher exergy efficiency, better utilisation of low temperature sources and higher system efficiency. Due to its multiple advantages, LTDH can contribute significantly in decarbonising the heating sector. The impact of lowering supply temperature in a DH system is quantified using a simulation. A small DH system with 50 households where heat is supplied by a centralised heat pump is simulated. The results of the simulation show that there is a reduction in energy consumption and peak electricity load as the temperature of the DH system is reduced. The impact of lower temperature on exergy efficiency of the DH system is also shown. The chapter identifies the main challenges and discusses some solutions for lowering the temperatures in existing DH systems. Some of the possible ways of implementation of LTDH are discussed followed by examples of LTDH across the world. The chapter concludes that LTDH can play an important role in decarbonising DH systems in the future. Chapter 2 - Using energy effectively is one of the most important issues and problems that countries should take up. As a parallel of increasing energy demands worldwide and still mostly using fossil fuels, energy saving issues have gained much importance in recent years for all areas of life. It is a fact that construction is also an important role in the emergence of the energy and environmental problem that we see as the problem of our centenary. As buildings consume about 40% of the world's

Preface

ix

annual energy consumption globally, this study will focus on the evaluation of residential heating system alternatives using fuzzy numbers. Multi-Criteria Decision Making (MCDM) techniques, fuzzy AHP and fuzzy ANP methods are used for evaluation and the results of both algorithms are compared. The main contribution of this paper is to select residential heating system alternatives using numerical methods with experts’ view. Chapter 3 - LPG (liquefied petroleum gas) is a fuel-efficient, high-heat valuable and scarce resource. This research is aimed at designing a logistics system for X Gas Company to ensure efficient distribution of LPG, which begins with the ordering process and ends with the placement of stations in Istanbul-Turkey, taking into account the storage, preparation, loading and delivery operations of X Gas Company. Because gas as energy is a scarce resource, the primary performance indicators are distribution, time, cost, and stocklessness. Thus, the ultimate goal is to increase retailer and consumer satisfaction. The aim of this study is to develop a systematic approach using engineering tools and optimization software. One of the biggest problems in this study is the “Vehicle Routing Problem”. The optimal route was found using an optimization software for the Vehicle Routing Problem and the routing costs were reduced. After the design of the routing system, a decision support system has been submitted in order to respond to future changes related to the current system. Finally, the designed program was run for different scenarios and the results were analyzed. In light of these studies, it has been proven that a high amount of saving improvement is possible within the current system. Chapter 4 - Three types of electro heating skin-systems are presented in the article. The main features of skin heating systems are considered. The advantages of these systems for heating extra-long pipelines transporting oil, gas, water and other liquids are shown.

In: Heating Systems Editor: Elias Moore

ISBN: 978-1-53617-557-8 © 2020 Nova Science Publishers, Inc.

Chapter 1

DECARBONISING HEATING SYSTEMS: THE ROLE OF LOW TEMPERATURE DISTRICT HEATING Kapil Narula, PhD Energy Efficiency Group, Institute for Environmental Sciences (ISE), University of Geneva, Geneva, Switzerland

ABSTRACT It is estimated that heating and cooling contribute to approximately half of the global final energy demand. This is likely to increase in the future due to increase in urbanisation and climate conditioned buildings. Boilers using coal, oil and natural gas contribute to the majority of heat supply which results in large GHG emissions. It is therefore essential that heating (and cooling) supply is decarbonised in a carbon constrained world. District heating (DH) system is an effective way of supplying heat in cities having areas with high heat demand density. This chapter examines the role of low temperature district heating (LTDH) in decarbonising heating systems. It presents an overview of the heating demand and supply and emphasises the need for decarbonising heating 

Corresponding Author’s E-mail: [email protected].

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Kapil Narula systems in the background of recent global committments to mitigate climate change. The chapter discusses the development of different types of DH systems. It highlights the main features of a LTDH and discusses its potential for supplying decarbonised heat. The main advantages of LTDH over existing DH systems are explained which include lower heat losses, higher exergy efficiency, better utilisation of low temperature sources and higher system efficiency. Due to its multiple advantages, LTDH can contribute significantly in decarbonising the heating sector. The impact of lowering supply temperature in a DH system is quantified using a simulation. A small DH system with 50 households where heat is supplied by a centralised heat pump is simulated. The results of the simulation show that there is a reduction in energy consumption and peak electricity load as the temperature of the DH system is reduced. The impact of lower temperature on exergy efficiency of the DH system is also shown. The chapter identifies the main challenges and discusses some solutions for lowering the temperatures in existing DH systems. Some of the possible ways of implementation of LTDH are discussed followed by examples of LTDH across the world. The chapter concludes that LTDH can play an important role in decarbonising DH systems in the future.

Keywords: district heating, decarbonisation, exergy, low tempertaure

INTRODUCTION It is estimated that heating and cooling contribute to approximately half of the global final energy demand. About fifty percent of the heat demand is from the industrial sector for high temperature processes, hot water, drying and similar applications. The remaining heat is used in buildings for space heating (SH), domestic hot water (DHW), and cooking. A minor share of heat is used in the agricultural sector [1]. The demand for heating and cooling is primarily dependent on the weather (geographical location, ambient temperature, humidity etc.) and socio-economic characteristics (income levels, infrastructure, heating choices etc.). However, modern buildings have some sort of heating and cooling for higher comfort of the occupants. Energy demand in buildings is a function of the building envelope (thermal properties, air flow, lighting levels, energy efficiency etc.) and electricity is extensively used for

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providing cooling, lighting and ventilation services. Heating on the other hand, is mainly provided by primary energy sources other than electricity. SH and DHW production in buildings contributed to about 60% (36 EJ) of final energy demand in countries with cold climate (OECD countries excluding Australia, Mexico, New Zealand and Israel and nonOECD countries in Europe and Eurasia) and about 43% (24 EJ) in countries with moderate and warm climate (2010) [2]. Specifically in the residential sector, heating contributed to 80% (SH-65%, DHW-15%) of the final energy demand in 28 countries of the European Union (EU) (2017) [3]. According to the U.S. residential energy consumption survey (RECS) 2015, about 17% of the energy consumed in the residential sector was from air conditioning, 15% was from SH, 14% from DHW, 10% from lighting and the balance was used in electrical appliances [4]. Buildings are generally heated using decentralised oil or natural gas fired boiler, electric heater and heat pumps (HPs). District heating (DH), where heat is generated centrally and distributed over large distances by a district heating network is also an important way of heating buildings especially in north European countries. Some DH systems use waste heat from nuclear plants, waste incinerators, combined heat and power (CHP) plants and, geothermal heat. Ambient heat from rivers, lakes, waste water treatment plants, sea water and air can also be harnessed using large HPs and this heat can be further distributed by DH networks. It is estimated that globally, only about one-tenth of heat was produced from renewable energy source (RES) (e.g., biofuels and solar heating) and fossil fuels continue to be the largest heating source [5]. The situation in EU-28 countries is relatively better and about 75% of heating (and cooling) was generated from fossil fuels while 19% was generated from RES [6] . Globally, in 2017, 59% of heat in buildings was provided by fossil fuel boilers and 22% was supplied by electric heaters. The remaining heat was supplied by DH (10%), RES (6%) and HPs (3%) [7]. As heating (and cooling) is largely dependent on fossil fuels (including electricity generated from fossil fuels), it leads to large greenhouse gas (GHG) emissions. It is estimated that heating (and cooling) contribute to about 40 percent of the energy related CO2 emissions [8].

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Recent international agreements have shown an urgency to undertake collective action on decarbonisation. In order to lower emissions, countries agreed on global peaking of GHG emissions, ‘as soon as possible’, and attaining GHG emissions neutrality in the second half of the century, as a part of the Paris climate agreement in 2015 [9]. The EU had committed to lowering GHG emissions to 40 percent below 1990 levels by 2030 at the Paris climate agreement, but had no definite goal for 2050. Raising this ambition, the European Commission (EC) proposed a ‘European Green Deal’ in December 2019, which raises the 2030 target of reduction of GHG emissions to 50-55 percent and aims to reach net-zero GHG emissions by 2050 (for EU-28 countries) [10]. Decarbonising the energy sector, building renovation and coupling of the electricity, gas and heating sectors are some of the key features of the proposal. If the proposal is accepted in March 2020, it is likely to be enshrined in form of a ‘climate law’, and various EU regulations such as the renewable energy directive, energy efficiency directive, emissions trading directive etc. will be aligned with the new emission reduction targets. Apart from the EU, over 60 countries have committed to reach net zero GHG emissions by 2050 [11]. Further, nearly 10,000 cities have adopted targets to reduce GHG emissions and about 250 cities aim to have 100% RES in atleast one of the sectors of heating/cooling, power and transportation [12]. Considering the high use of fossil fuels in the heating sector and the recent political commitments, it is evident that the heating sector will have to be decarbonised in the near future. This chapter examines the role of low temperature district heating (LTDH) in decarbonising heating systems. The next section discusses the development of DH system and the advantages of LTDH over existing DH systems are enumerated. The impact of lowering supply temperature in a DH system is quantified with a simulation in the subsequent section. The chapter goes on to discuss the main challenges and solutions for lowering temperature levels in existing DH systems. Some ways of implementing LTDH are discussed followed by examples of LTDH in the penultimate section, before concluding the chapter.

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DISTRICT HEATING SYSTEM A DH system supplies hot water (or steam) to the building thermal system from a heat generation system outside the building [13]. A DH network transmits heat from a centralised location where heat is generated, through a series of underground insulated pipes to a number of remote buildings. The buildings can be residential (single family house (SFH) or multifamily house (MFH)), commercial spaces, schools, offices etc.

Temperature Levels DH systems have evolved over many decades and have been classified by temperature levels. First generation DH systems which were in use in early 20th century distributed steam at around 200°C using steam pipes in concrete ducts. This was followed by the second generation (2G) DH systems which were pressurised hot water systems operating at above 100°C and were in use around the middle of the 20th century. Third generation (3G) DH systems used medium temperature heat supply (80 100°C) and form the majority of DH systems in use today. These systems typically use primary and secondary closed hydraulic loops with water as the working medium. Heat exchangers in heating substations are used to thermally couple the two systems and hydraulic pumps maintain suitable water flow on the primary and secondary side. The fourth generation (4G) heating systems, also called as ‘low temperature networks’ carry water at 30-70°C and have emerged since the past two decades or so [14]. Heating networks which operate at 15-20°C are known as 5G, cold district heating (CDH), ultra low temperature DH (ULTDH) systems or as DH systems with “neutral” temperature levels [15], [16], [17]. These networks carry cold water and are used for both cooling and heating. A LTDH is considered as “a system of district heat supply network and its elements, consumer connections and in-house installations, which can operate in the range between 50-55°C to 60-70°C supply temperatures and 25-30°C to 40°C return temperatures to meet consumer demands for

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thermal indoor comfort and domestic hot water” [13]. This temperature range is considered sufficient for meeting the heat demand in existing as well as low energy buildings. The temperature ranges in LTDH systems are not defined clearly and often 50°C is considered as the upper limit in the network. Lower temperature levels are also feasible in CDH which has been defined as “a system for distributing cold water in a temperature range between 10-25°C to end-users’ substations where it is used to produce, also simultaneously, hot and cold water at different temperatures and for different purposes (SH, cooling, DHW production) via heat pumps and chillers” [15].

Heat Supply Potential It is estimated that there are approximately 80,000 DH systems worldwide, and about 6000 of these are in Europe [18]. The total length of distribution pipelines in DH systems have been estimated as 600,000 kms globally and about 200,000 kms in Europe. Industries (45%) and buildings (51%) consume the major share of heat from DH and Russia, China, and the EU accounted for 85% of these heat deliveries. DH supplied only 8% of the heat globally and 13% of the heat in the EU. However, DH systems supply a high share of heat (above 50%) in Iceland, Denmark, Sweden, Finland, Estonia, Latvia, Lithuania, Poland, Russia, and northern China [18]. The project ‘Heat Roadmap Europe’ (HRE) [19] mapped the heating and cooling demand for 28 member states of the EU and estimated that the heating demand in buildings (in 14 countries which were evaluated) could theoretically be met by waste heat from electricity generation [20]. Large scale decentralised heating systems supplied by RES were recommended for supplying the heating demand. The project concluded that increasing the share of DH is crucial as it enables better integration of RES and excess heat sources. It was estimated that in the 14 evaluated countries DH can cost-effectively provide at least 50% of the heating demand in 2050 (share was about 12% in 2015). Up to 70% market share of DH was considered as

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feasible and was recommended (depending on the heat demand density in urban areas) in different countries. The HRE project estimated that it is possible to reduce the use of fossil fuels in heating by about 10 PWh (compared to 2015) and estimated that CO2 emissions can be lowered by 86% (compared to 1990) under the HRE 2050 scenario. The HRE 2050 scenario achieved decarbonisation in the heating (and cooling) sector by implementing ambitious renovation of the existing building stock, energy efficiency measures, development of DH & cooling grids, use of efficient HPs, energy supply from excess heat and RES, as well as better integration of heating with other energy sectors [21]. It was reported that extending the HRE 2050 scenario to a ‘smart energy system’ could provide a pathway towards 100% decarbonisation of the EU. It was also estimated that an integrated approach to decarbonise the sector reduces energy system costs by around 6% (67.4 billion €) annually, as compared to a conventionally decarbonised system. The project concluded that while decarbonisation of heating (and cooling) sector is technically feasible, its implementation is hindered by an ethical, political and organisational failure. Considering the case of a specific country, three strategies, viz. reduction of specific space heating demand in buildings; integrating RES and increased heat distribution by DH; and use of HPs were examined for decarbonising the Swiss heating system [22]. However, as specific heat demand in buildings will continue to decrease due to building renovation, the economic competitiveness of existing DH system is likely to reduce in the foreseeable future. In the Swiss context, the potential percentage of demand supplied by high temperature DH system was shown to decrease from 66% to 41% in the considered scenario of reduced demand in buildings while the potential for LTDH systems increased significantly from 2.1% to 42% [23]. As LTDH systems can supply end user heating demand even in low heat demand scenarios, they have an important role to play in decarbonising heating systems in the future.

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ADVANTAGES OF LTDH Various authors have highlighted the importance of LTDH for future energy systems [24],[25],[26]. 4G heating systems with supply temperature at 50°C and return temperature at 20°C (annual averages) were envisaged for future sustainable energy systems [27]. The main advantages of LTDH over the current DH systems are as follows.

Lower Heat Losses Heat loss in a DH system depends on various factors such as network supply temperature, ambient temperature, heat density, type of piping and insulation, size of the DH system etc. As LTDH systems have lower distribution temperatures, it leads to lesser heat loss in pipelines. The potential for LTDH implementation in Norway was examined and the authors concluded that heat loss in the network could be reduced by lowering the supply temperature from 80°C to 55°C [28]. Lower heat losses lead to higher cost competitiveness of DH system as compared to alternative heat sources and increases the potential of deployment of LTDH.

Better Quality Match between Heat Supply and Heat Demand and Higher Exergy Efficiency Exergy can be defined as the maximum amount of work that can be extracted from a system. The existing DH systems which directly use fossil fuels distribute hot water at a supply temperature greater than 80°C, which is much higher that the temperature required for SH and DHW at the customer end. Use of high quality energy sources such as gas and oil for providing relatively low quality SH and DHW demand is exergy inefficient and must be avoided. LTDH systems typically have a supply temperature close to 50°C which can be provided by low temperature heat sources.

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Lower supply temperatures and use of low temperature heat sources leads to a better match between heat supply and heat demand and results in higher exergy efficiency.

Better Utilisation of Low Temperature Heat Sources Lower supply temperature in LTDH allows the use of low temperature waste heat from industrial processes. Further, they can easily integrate low temperature geothermal heat or solar heat directly into the DH system. This leads to increased utilization of waste heat and better utilisation of low temperature sources (including RES) in the heat supply mix, contributing to decarbonisation of heat supply.

Higher Power-to-Heat Ratio in Steam CHP Plants Lower temperature in LTDH lead to improved power to heat ratio in the CHP plant [29]. A low supply and return temperature in the DH allows more power to be extracted from steam expansion processes at the same heat load leading to a higher power to heat ratio in steam CHP plants.

Direct Flue Gas Condensation Leading to Higher Heat Utilization Lower return temperature in LTDH allows direct condensation from combustion flue gases especially in biomass and waste-based CHP plants. Higher heat recovery through condensing heat recovery on exhaust gases, and use of other heat recovery measures, can lead to an increase in heat recovery by up to 25% [29].

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Greater Utilization and Reduced Heat Loss in Thermal Storage Units Hot water storage tanks may be used in DH systems for short term and seasonal heat storage. Lower DH temperature implies that hot water can be stored at lower temperature in the tanks. This leads to lower heat loss in the tank as the temperature difference between the stored water and ambient air is lower. Further, a lower return temperature allows higher utilisation of heat storage capacity of the thermal storage [31].

Higher Efficiency Lower supply temperature in DH allow a higher coefficient of performance (COP) of the HP. This lowers the electricity consumption and improves the efficiency of the HP. The conversion efficiency of solar thermal collectors is also higher if the difference between the average temperature of the working fluid in the solar collector (equal to DH supply temperature) and the ambient temperature is lower. Higher efficiency of system components increases the overall efficiency of the DH system.

Reduced Thermal Stress on Pipelines A lower DH temperature reduces thermal stress on the heat distribution pipelines. This leads to lesser chances of leakages in pipes resulting in reduced maintenance costs. Pre-insulated flexible twin plastic pipes can replace conventional material such as steel and copper in the existing heat distribution pipelines. The thickness of the insulation material required for the pipelines is also lesser due to use of lower temperatures.

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Lower Risk of Scalding Existing DH networks carry water at high temperature. In case of a leakage, there is an increased risk of scalding if humans come in contact with water having a temperature greater than 65°C. However, this risk is eliminated in case of LTDH as the supply and return temperatures are lower.

Future Ready Systems A networked and smart LTDH can be integrated with multiple heat sources such as a CHP plant, gas boiler, HP, biomass boiler and other components such as heat demand management systems, thermal storage and building control systems. Better control on heat demand and different options for heat generation improve the efficiency of the entire system and enables optimal utilisation of resources. Real-time monitoring and decision support tools can enhance the functionality of a smart LTDH to deliver high performance and optimal consumer comfort. Therefore, a smart LTDH system is considered as an enabler for a transition to a low carbon society [29]. Due to its multiple advantages, a LTDH system can contribute significantly to decarbonise the heating sector by replacing the use of fossil fuel boilers. This also lowers the environmental impact of heating and local air pollution which results in higher socio-economic benefits and better quality of life especially in cities.

SIMULATION OF IMPACT OF LOWER SUPPLY TEMPERATURE IN DH SYSTEM The impacts of lower supply temperature in DH system have been examined in Ref [32] which compared DH systems having three

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temperature levels: LTDH (55/25°C), ULTDH with electric boosting (45/25°C), and ULTDH with heat pump boosting (35/20°C) taking into account the grid losses, production efficiencies and building requirements. Ref. [33] examined LTDH systems using four representative case studies from Austria: Aktivpark Güssing, Seestadt Aspern, Winklweg Siedlung and Hummel Kaserne. The study examined different supply and demand connection schemes, different local framework conditions such as heat consumption and production settings and different control strategies. The results of the study showed that the availability and economic conditions of low temperature heat sources is a key factor for facilitating LTDH systems.

Description of Simulated DH System In order to examine the impact of lower supply temperature in a DH system, a neighbourhood having 50 MFH is considered. The average (yearly) supply temperature of this DH system, T DH, is varied from 90 to 40°C and its impact is assessed. The simple schematic configuration of the simulated DH system for the neighbourhood is shown in Figure 1. The physical flows of hot and cold water are shown in bold lines while heat flows are shown in dotted lines. The heat demand of the MFH consists of SH demand and DHW demand. A centralised air-water HP powered by electricity is used to provide heat to the DH system. The primary hydraulic loop provides hot water via a buffer tank to the customer substation located in the basement of the building. A heat exchanger in the substation is used to extract heat from the primary loop and supply it to the secondary loop. The secondary hydraulic loop circulates hot water in the radiators fitted in individual rooms for space heating. An insulated hot water tank is used to store DHW at a temperature of 55-60°C which is used in kitchen and bathrooms on as required basis.

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Figure 1. Simple schematic configuration of simulated DH system.

Hot water consumption per inhabitant in Europe varies between 90160 litres/day and the total demand of DHW depends on the number of inhabitants. Hourly SH demand is a function of specific heat demand of the building, heated surface area, external temperature and the required temperature inside the apartment. The average heated surface area (Aavg) of a MFH in Switzerland is around 76 m2, annual specific heat demand (Qspec) (SH and DHW) is around 106 kWh/m2, and the total heat demand over the entire year for the simulated case is around 8 MWh. The heat demand for hour ‘h’, Qdem(h), for the neighbourhood having 50 MFHs was simulated as explained in Ref [34] and is provided exogenously for this simulation. The centralised HP delivers hot water at TDH to the DH system. It is assumed (for simplicity) that TDH is constant over the year as the DH system is operated in the constant temperature mode. There are losses in the heat distribution network but as the DH system is small, heat loss in the DH system, QDH.loss, is assumed to be 10% (loss = 0.1). Heat supplied by the HP, QHP(h), consists of ambient heat, Qamb(h), and heat supplied by electricity, Qel (h). Heat supplied by the HP in a specific hour ‘h’, is given by Equation 1.

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Kapil Narula QHP (h) = (1+ loss) ∙ Qdem (h)

Equation 1

The total thermal energy delivered by the HP in a specific hour, is given by Equation 2. QHP (h) = Qamb (h) + Qel (h)

Equation 2

The hourly COP of the HP is a function of the ambient air temperature Tamb (h) and TDH. Equation 3 gives the maximum theoretical efficiency of the HP, also called as the Carnot efficiency of the HP which is achieved in an ideal case. COPCarnot (h) = [TDH + 273] / [TDH – Tamb (h)]

Equation 3

However, due to a non-ideal compression cycle, HP losses etc. the actual COP of the HP is given by Equation 4 COPHP (h) = ηHP ∙ COPCarnot(h)

Equation 4

where ηHP is the efficiency of the HP and is assumed as 50%. The electrical energy consumed by the HP in the specific hour is calculated using Equation 5 and the ambient energy used by the HP is calculated using Equation 6. Qel (h) = QHP (h) / COPHP (h)

Equation 5

Qamb (h) = QHP (h) ∙ (1-1/COPHP (h))

Equation 6

The seasonal COP (SCOP) of the HP is given by Equation 7. SCOP = Total QHP/ Total Qel

Equation 7

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Salient Parameters for Considered Case The HP supplies the entire heat demand for the MFH. For this simulation hourly ambient temperatures, Tamb(h), in 2017 at Geneva are used which are representative of ambient temperature for locations in central Europe. The salient parameters for the considered case having 50 MFHs are shown in Table 1, where Qmax, Qmin and Qavg are the maximum, minimum and the average (hourly) heat supplied by the HP. Total Qdem, Total QHP and Q̇max.HP (th) refer to the annual heat demand of the MFH, annual heat supplied by the HP and the maximum thermal capacity of the HP, respectively. Table 1. Salient parameters for the considered case Parameter Qmax (h) Qmin (h) Qavg (h) Total Qdem Total QHP Q̇max.HP (th)

Simulated value 128 kWh 4.5 kWh 45.8 kWh 401 MWh 441 MWh 140 kW

Figure 2. Hourly heat demand and ambient temperature.

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Figure 2 shows the hourly heat demand, Qdem(h) per MFH (left-hand y axis) and the ambient temperature, Tamb(h) (right-hand y axis) used for the simulation over the entire year. Hour 1 corresponds to 0000 - 0100h on 01st January of the year and hour 8760 corresponds to 2300 - 0000h on 31st December. It is evident that heat demand peaks in winter months when the ambient air temperature is low. Heat supply for SH is cut off when the average temperature crosses a certain predefined threshold (as summer approaches). Hence the head demand in summer months consists only of the DHW demand.

Simulation Results The contribution of ambient heat and heat provided by electricity in each hour is shown in Figure for the case when TDH is 40°C. The total heat supplied by the HP in the DH system over the entire year is 441 MWh (also see Table 1) and is represented by the total shaded area. Heat supplied by electricity, Qel, is around 90 MWh (20% share) while ambient heat supplied, Qamb, is 351 MWh (80% share) over the entire year. It is seen that the majority of the heat is provided by ‘free’ ambient heat and a LTDH system with a HP can potentially replace 80% of energy provided by fossil fuels by ambient heat. In order to examine the impact of lower DH supply temperature, TDH is lowered from 90 to 40°C in steps of 10°C and the relevant parameters are noted for different simulation runs. The first panel in Figure 4 shows the impact of different DH supply temperatures on the SCOP of the HP. As seen, the seasonal performance factor of the HP improves from 2.2 to 4.9 as TDH is lowered from 90 to 40°C, thereby improving the overall heating system efficiency. The second panel shows the share of heat supplied by different energy sources when different DH supply temperatures are used. It is seen that as TDH is lowered from 90 to 40°C, the share of ambient heat increases from 54% to 80% while the share of electricity decreases from 45% to 20%.

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This implies that about four-fifths of the heat delivered is ‘free’ when TDH is 40°C.

Figure 3. Heat supplied in DH system by different sources.

Figure 4. Impact of different DH supply temperatures.

Impact on Electricity Consumption Table 2 shows the percentage reduction in electricity consumption when TDH is varied (as compared to electricity consumed when TDH is 90°C) and the reduction in the peak electricity load on the grid. It is seen that the electricity consumed is reduced by about 55% when TDH is lowered from 90 to 40°C. This has related consequences such as equivalent

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reduction in GHG emissions (as emission factor of electricity is lower than fossil fuels) and reduction in primary energy consumption. Further, the peak load from the HP on the electricity grid is lowered from about 66 kW to 35 kW. This reduction in peak load on the electricity grid has multiple benefits such as smaller contracted load, leading to lower energy bills and avoided investment in strengthening electricity grid infrastructure (in case of inadequate grid capacity). Table 2. Percentage reduction in electricity consumption and peak electricity load for different DH temperatures TDH (°C) 90 80 70 60 50 40

Reduction in electricity consumption (%) 0.0% -9.7% -20.0% -30.9% -42.4% -54.8%

Peak electricity load (kW) 66.37 60.93 55.17 49.06 42.58 35.68

Impact on Exergy Efficiency One of the advantages of LTDH systems is higher exergy efficiency. This can be calculated in the simulated case. The exergy factor of heat supplied in the DH system (ε) can be given by Equation 8 [35]. ε = 1-

TO (TDH –TR)

TDH

+ ln ( T ), R

Equation 8

Where, TO is the reference average ambient temperature, TDH is the average supply temperature and TR is the average return temperature in the DH system (all temperatures in Kelvin degrees) [36]. The exergy factor of heat (exergy loss) in the DH system should be low for higher exergy efficiency. Table 3 shows the calculated exergy factor for different combinations of TDH and TR. TO is taken as 10°C (average ambient temperature during the year). As seen, the exergy factor of heat supplied decreases from

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around 19% (90/50°C) to 7% (40/20°C) with the given combinations of TDH and TR, implying that the exergy efficiency of LTDH systems is higher. Lastly, lowering of TDH or TR, both lead to increase in exergy efficiency. Therefore, measures should be taken to lower the supply and return temperature in existing DH systems. Table 3. Calculated exergy factor for different temperatures TDH (°C) 90 80 70 60 50 40

TR (°C) 50 40 35 30 25 20

Ε (%) 18.6% 14.9% 13.0% 10.9% 8.8% 6.6%

CHALLENGES IN LOWERING TEMPERATURES IN DH Ref [14] identified various challenges for future DH systems. Primarily among them were the ability to distribute heat with low grid losses and the ability to include heat from low-temperature RES. Ref [28] also examined the challenges and the potential for implementation of LTDH in Norway. Some of the main challenges in attaining lower supply and return temperatures in existing DH systems are discussed ahead.

High Specific Heat Demand in Buildings Poor insulation in buildings leads to high heat loss. This results in high annual specific heat demand in buildings. Hence, radiators fitted inside the rooms require high temperatures and increased rate of flow of hot water. Therefore, the existing DH systems which burn fossil fuels are designed for providing high temperatures. Older buildings typically have high specific heat demand which is one of the hurdles in lowering temperatures in DH systems.

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Hot Water Storage at Temperature Greater Than 55-60°C DHW is stored in hot water tanks in the building or at a nearby substation. It is recommended for health reasons that this water should be stored at a temperature greater than 55-60°C so as to avoid the growth of legionella bacteria and other microorganisms in potable water. This implies that the supply temperature in the DH network has to be higher than 60°C (to account for heat losses in distribution network).

Improper Heating System Design The hydraulic system may be poorly designed (at the distribution system side) leading to direct water flow between the supply and return pipes. If bypass valves are used to control the flow of hot water during period of low heat demand, it results in a hot water line being directly connected to the return line without passing through any heat exchanger in a substation. This may lead to a high return temperature in the DH system. There may be mismatch in heat demand from the designed value or the system may be improperly designed. Heat exchangers may be installed wrongly, could be improperly sized with inadequate surfaces, temperature sensors could be installed at wrong places or control values may be under/oversized.

Errors at Substation and Customer Heating System Errors may occur at different places in the heating system. These errors may be in the distribution system (primary loop), at the customer exchange station or in the customer heating system (secondary loop). Substation faults may occur due to malfunctioning of sensors and valves, and due to wrong choice of set point temperature. Temperature errors in distribution networks and improper functioning of components such as thermostatic valves can also result in higher return temperatures. Fouling of heat

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exchangers and non-functional control valves are some of the other common faults which lead to higher return temperature. A higher return temperature leads to a higher supply temperature as it is essential to maintain an effective heat exchange in the substation [13],[29].

SOLUTIONS FOR LOWERING TEMPERATURES IN DH In order to achieve lower annual average return temperatures in new residential buildings, some technical improvements were proposed [37]. These included three-pipe distribution networks, apartment substations, and longer thermal lengths for heat exchangers. Ref [13] provided guidelines for the development of LTDH and a transformation roadmap from high to LTDH systems was presented in Ref [39]. Some of the measures for lowering temperatures in DH systems are as follows.

Deep Retrofits of Building Envelope A lower specific heat demand in buildings implies smaller heat flows. This can be achieved by deep retrofits of the building envelope and constructing low energy demand houses. Along with this, use of efficient heat transfer technologies such as underfloor heating coils, wall and ceiling radiant panels or large efficient radiators can result in better heat transfer. It is also recommended that existing radiator sizes should be retained when undertaking refurbishment of the building. It has been shown that replacement of radiators in a building after refurbishment is not necessary. The size of the radiator used in a building with a specific heat demand of 70 W/m2 supplied by a DH system with 75/65/20°C (TDH winter/TDH summer/TR) will be similar to a radiator designed for a refurbished building with a specific heat demand of 15 W/m2 for a LTDH system operating at 55/25/20°C [13].

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Segregation of DHW and SH Supply Storage of DHW at a temperature of 55-60°C prevents the use of LTDH systems. One way to overcome this hurdle is to segregate the DHW from SH supply. A three pipe system can be used to separate the supply of SH and DHW. A constant temperature is maintained in the DHW pipe while temperature in the SH pipe can be varied depending on the external temperature. This segregation of DHW from SH supply ensures that lower temperatures can be used in DH systems. Another way is to supply water at the temperature required for meeting the demand of SH. DHW can be generated instantaneously at the required temperature by using micro heat pumps or electric heaters. When DHW is produced at site, the length of pipes should be short and the volume of hot water in the pipes should be restricted to less than 3 litres to prevent the risk of growth of Legionella bacteria. DHW circulation should be avoided and separate pipes should be used for each fixture providing DHW.

Other Measures Temperature errors in DH systems and substations need to be eliminated to achieve lower network temperatures. In existing buildings measures for lowering temperatures include optimization of heat distribution by hydraulic balancing and use of variable-speed pumps for controlling hot water flow. Monitoring of substations should be undertaken and building heating systems must be examined regularly to ensure that control equipment and sensors are working correctly. Heat exchangers with long thermal lengths should also be used in heating substations. A longer thermal length implies better ability to transfer heat from primary to secondary side and hence results in a lower return temperature [39].

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Replacing District Heating Pipes Better insulation material having a lower thermal conductivity (λ) such as polyurethane rigid foam (PUR) (λ = 0.024 W/mK) can replace existing insulation material such as glass wool (λ =0.033-0.04 W/mK) in heat distribution pipes. This leads to lower heat losses and has an impact of marginally lowering the supply temperature in the DH system. The diameter of the pipe depends on the heat demand and the volume of water required to deliver the heat. Twin pipes used in the LTDH system are preinsulated and have a smaller diameter which further leads to reduced heat losses and helps in lowering the supply temperature.

IMPLEMENTATION OF LTDH Development of LTDH should proceed along with existing DH systems. The LTDH system can be an independent small scale system which is connected to new low energy buildings and is supplied by low temperature heat sources. Such a greenfield project is the easiest in terms of design and implementation. Another way could be to use the return line of an existing DH system to feed the supply line of the new LTDH and to operate the LTDH as a subnet. The return line of the new subnet is connected to the return line of the original DH system lowering the return temperature of the existing DH, thereby extracting maximum heat. Adoption of this scheme allows expansion of the existing DH system to new areas without increasing the capacity of the underground pipes in the functional DH network. Thus a larger number of consumers can be connected without additional cost for replacement of the existing pipes. Small thermal networks which connect a few buildings can also be converted to LTDH system by replacing the existing boiler with a centralized HP. Building renovation plays an important role in this case and such a system is suitable for buildings having low energy demand. As

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energy efficiency measures are implemented in buildings in a phased manner, the temperature in existing DH systems can be gradually lowered. Table 4. Salient features of selected LTDH systems Location

Temperature Heat source Supply/Return

Spjald, Denmark

65/32°C: heating season; 65/39°C: nonheating season 65/36°C: entire year

Tarm, Denmark

Malling, Denmark

Middelfart, Denmark

68/38°C: heating season; 60/42°C: nonheating season 68/44°C: entire year

Location

Temperature

Feldlager, Kassel, Germany (Planned)

40°C supply

LowEx 40/25°C subnet (Sonnenberg-

No. of Type of buildings heating in buildings 582 Mostly direct (mostly system with old) radiators

Other measures

Wood chips boiler with flue gas condensation (main source), solar, absorption HP, wood pellet boiler. Back up gas and oil boilers Transmission network from central cogeneration plant Transmission network – mainly from cogeneration plant and industrial waste heat Heat source

1853 (mostly old)

Mostly direct system with radiators

DHW storage with built in heaters

1693

Mostly direct system with radiators

5015 (mostly old)

Mostly direct system with radiators

Highly efficient heat exchangers in SFH for DHW Instantaneous heat exchanger unit for DHW

No. of buildings

Geothermal (boreholes with central HP), solar

130

Type of heating in buildings Floor heating systems/low temperature radiators

Return line of existing supply system: 40-45°C

SFH and MFH

Natural gas

-

-

Other measures

Small water storage tanks, intelligent storage systems, thermal load shifting DHW: solar thermal system + electrical heater DHW: solar thermal with additional

Decarbonising Heating Systems Südwest) Ludwigsburg Germany (Planned) Wüstenrot, Germany

-

Location

Temperature

Lystrup, Aarhus, Denmark

55/25°C

Sønderby, Denmark

55/25°C

Near surface geothermal system with decentralized HPs

25 new high energy standard buildings (SFH and detached houses). Heat source No. of Type of buildings heating in buildings Existing supply 40 HTDH + return line terraced of this LTDH houses with lowenergy standard Existing return line 75 single Floor heating HTDH (45°C) + family supply line of houses HTDH

25 electric heating (direct flow heaters or micro HP) Common intelligent load/storage management system, with hot water and electricity storages Other measures

Substations with instantaneous heat exchanger unit and DH storage tank unit -

Data source: [39], [42].

Concurrent operation of DH systems with different temperature levels is essential for gradually moving from the existing DH systems to LTDH systems. Isolation of DH systems allows greater control over the supply and return temperatures in the DH systems. Different DH systems can then be cascaded and operated simultaneously at different temperature levels. Such a DH system is operated by SEMHACH, in the cities of ChevilleLarue and L’Hay Les Roses (suburbs of Paris) where five different temperature levels are used. Different customers are connected at different temperatures (very high, high, medium, low and very low) with the DH network [39]. Such a cascaded multi-level temperature system allows a lower return temperature in the DH system and improves the efficiency of the system. Ref [41] examines the implementation of ULTDH and LTDH systems and the impact of ground thermal energy storage. CDH systems have been

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implemented in towns of Oberwald, Kaltbrunn, Minusio and Trimbach (Switzerland); Wüstenrot, Aurich, Troisdorf and Jenfelder Au district in Hamburg (Germany); Berlingo, Sale Morosino and Bologna (Italy); and Hague (Netherlands) [15]. Some examples of DH systems where lower supply and return temperatures have been achieved along with their salient features are listed in Table 4.

EXAMPLES OF LTDH SYSTEMS There are various examples of LTDH systems worldwide. Ref [40] examines the application of LTDH for the new housing area “Zum Feldlager” at Kassel, Germany.

CONCLUSION Heating will continue to contribute to a high share of the global final energy demand in the future. Currently the major share of heat is supplied by fossil fuels leading to large GHG emissions. Considering the recent political commitments on achieving net zero emissions, heating supply will have to be decarbonised in the near future. A DH system is an efficient way of providing heat to buildings and has a good potential to supply decarbonised heat. However, the existing supply temperature in DH systems are much higher than the temperature required at the customer end for meeting SH and DHW demand. A LTDH operating at a supply temperature of around 50°C and return temperatures around 25°C can meet consumer heating demands in existing as well as low energy buildings. There are many advantages of LTDH such as lower heat losses; better quality match between heat supply and heat demand; higher exergy efficiency; better utilisation of low temperature heat sources; higher power-to-heat ratio in steam CHP plants; reduced heat loss in thermal storage units; higher system efficiency; reduced thermal stress on pipelines and lower risk of scalding. Due to lower supply temperature, a LTDH system can easily integrate low temperature heat

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sources and can therefore contribute significantly to decarbonising the heating sector by replacing the use of fossil fuel boilers. Simulation of different temperature levels in a DH system shows that energy consumption is reduced by about 55% when the supply temperature in the DH system is lowered from 90 to 40°C. Further, the peak load from the HP on the electricity grid is reduced from about 66 kW to 35 kW. The exergy factor of heat supplied decreases from around 19% (90/50°C) to 7% (40/20°C) implying that the exergy efficiency of LTDH systems is higher. Although LTDH systems has many benefits, there are many challenges in attaining lower supply and return temperatures in existing DH systems. These include high specific heat demand in buildings; requirement to store hot water at a temperature greater than 55-60°C; poor heating system design; and temperature errors at substation and customer heating system. These hurdles can be overcome by undertaking deep retrofits of building envelope to reduce specific heat demand, segregation of DHW and SH supply and by eliminating temperature errors in the heating system. There are various ways of implementing LTDH systems. DH systems can also be cascaded and operated simultaneously at different temperature levels. As lower temperature levels improve the system efficiency, efforts must be taken to lower the supply and return temperature in all existing DH systems. In all probability, LTDH systems will work concurrently with the existing high temperature DH systems in the near future. Such LTDH systems are being operated in various countries and salient features of selected LTDH systems show that these systems can be successfully implemented. As LTDH systems can supply the end user heating demand even in low heat demand scenarios, they can play an important role in decarbonising heating systems in the future.

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[29] Li, H;, and S J Wang. 2014. “General Rights Challenges in Smart Low-Temperature District Heating Development.” Citation 61: 1472–75. https://doi.org/10.1016/j.egypro.2014.12.150. [30] Dalla Rosa, Alessandro, Hongwei Li, Svend Svendsen, Sven Werner, Urban Persson, Karin Ruehling, Clemens Felsmann, Martin Crane, Robert Burzynski, and Ciro Bevilacqua. 2014. “Toward 4th Generation District Heating: Experience and Potential of LowTemperature District Heating.” IEA DHC/CHP. [31] Narula, K., Filho.F. De Oliveira, W. Villasmil, and M.K. Patel. 2019. “Simulation Method for Assessing Hourly Energy Flows in District Heating System with Seasonal Thermal Energy Storage.” Renewable Energy, (In press). https://doi.org/10.1016/J.RENENE.2019.11.121. [32] Lund, Rasmus, Dorte Skaarup Østergaard, Xiaochen Yang, and Brian Vad Mathiesen. 2017. “Comparison of Low-Temperature District Heating Concepts in a Long-Term Energy System Perspective.” International Journal of Sustainable Energy Planning and Management 12 (0): 5–18. https://doi.org/10.5278/ijsepm.2017.17.x. [33] Köfinger, M., D. Basciotti, R. R. Schmidt, E. Meissner, C. Doczekal, and A. Giovannini. 2016. “Low Temperature District Heating in Austria: Energetic, Ecologic and Economic Comparison of Four Case Studies.” Energy 110 (September): 95–104. https://doi.org/10.1016/ j.energy.2015.12.103. [34] Narula, Kapil, Fleury De Oliveira Filho, Jonathan Chambers, Elliot Romano, Pierre Hollmuller, and Martin K. Patel. Assessment of techno-economic feasibility of centralised seasonal thermal energy storage for decarbonising the Swiss residential heating sector. (Under review). [35] Gong, M, S Werner.2015. “Exergy Analysis of Network Temperature Levels in Swedish and Danish District Heating Systems.” Renewable Energy, no. 84: 106–113. https://doi.org/10.1016/j.renene. 2015.06.001. [36] Gong, Mei, Göran Wall, and Sven Werner. 2012. “Energy and Exergy Analysis of District Heating Systems.” Proceedings of

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Kapil Narula DHC13, the 13th International Symposium on District Heating and Cooling. Averfalk, Helge, and Sven Werner. 2018. “Novel Low Temperature Heat Distribution Technology.” Energy 145: 526–39. https://doi.org/ 10.1016/j.energy.2017.12.157. Kaarup Olsen, Peter. 2014. “Guidelines for Low-Temperature District Heating.” EUDP 2010-II: Full-Scale Demonstration of LowTemperature District Heating in Existing Buildings, no. April: 1–43. Helge Averfalk, Sven Werner, Clemens Felsmann, Karin Rühling, Robin Wiltshire, Svend Svendsen, Hongwei Li, Jerome Faessler, Floriane Mermoud, Loic Quiquerez. 2017. “International Energy Agency Technology Collaboration on District Heating and Cooling Including Combined Heat and Power Annex XI Final Report Transformation Roadmap from High to Low Temperature District Heating Systems.” Annex XI Final Report -Transformation Roadmap from High to Low Temperature District Heating Systems. Schmidt, Dietrich, Anna Kallert, Janybek Orozaliev, Isabelle Best, Klaus Vajen, Oliver Reul, Jochen Bennewitz, and Petra Gerhold. 2017. “Development of an Innovative Low Temperature Heat Supply Concept for a New Housing Area.” In Energy Procedia, 116:39–47. Elsevier Ltd. https://doi.org/10.1016/j.egypro.2017.05.053. Dolna, Oktawia, and Jarosław Mikielewicz. 2020. “The Ground Impact on the Ultra-Low- and Low-Temperature District Heating Operation.” Renewable Energy 146 (February): 1232–41. https://doi.org/10.1016/j.renene.2019.07.048. IEA Annex TS1 - Low Temperature District Heating for Future Energy Systems. Successful implementation of innovative energy systems in communities - with low temperature district heating and renewable energy sources. IEA DHC/CHP.

In: Heating Systems Editor: Elias Moore

ISBN: 978-1-53617-557-8 © 2020 Nova Science Publishers, Inc.

Chapter 2

RESIDENTIAL HEATING SYSTEM SELECTION USING MCDM TECHNIQUES Yavuz Ozdemir*, PhD and Sahika Ozdemir, PhD Faculty of Engineering and Natural Sciences, Istanbul Sabahattin Zaim University, Istanbul, Turkey

ABSTRACT Using energy effectively is one of the most important issues and problems that countries should take up. As a parallel of increasing energy demands worldwide and still mostly using fossil fuels, energy saving issues have gained much importance in recent years for all areas of life. It is a fact that construction is also an important role in the emergence of the energy and environmental problem that we see as the problem of our centenary. As buildings consume about 40% of the world's annual energy consumption globally, this study will focus on the evaluation of residential heating system alternatives using fuzzy numbers. MultiCriteria Decision Making (MCDM) techniques, fuzzy AHP and fuzzy ANP methods are used for evaluation and the results of both algorithms are compared. The main contribution of this paper is to select residential heating system alternatives using numerical methods with experts’ view. *

Corresponding Author’s E-mail: [email protected].

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Yavuz Ozdemir and Sahika Ozdemir

Keywords: Fuzzy AHP, Fuzzy ANP, energy saving, heating systems, housing warming

INTRODUCTION As the needs increase in our developing world, we consume the resources we have in line with the demands. The environment, society and place which we live in are very important for this. Human beings feel the need to shelter, warm up, consume in their environment and reduce it by using available resources. To make the environment we live in permanent and livable, we need it so that we can leave a sustainable life for future generations. The need for energy is increasing day by day due to increasing population and developing technology. The limited level of energy resources presents great difficulties in meeting this increasing energy consumption. With the development of technology and increasing needs, the alternatives of residential heating systems have also changed over time. Residential heating systems, which have different fuel types and many forms of heating, are very much preferred because of their different characteristics. The central system, which emerged with the effect of time conditions and technology, has brought a different dimension to the residential heating systems. The fact that old-type coal stoves required a lot of human pursuits and only heated a single room intensified demand for centralised heating systems. Central system as well as more than one residential heating system is available. People who will choose for their house or company's heat may find it difficult to choose the best and most profitable of these residential heating systems. It is important to choose the heating system which is the least harmful to the environment and the least energy consumption when making these choices. Selecting or prioritizing alternatives from a set of available alternatives with respect to multiple criteria is often referred to multi-criteria decisionmaking (MCDM). MCDM is a well-known branch of a general class of

Residential Heating System Selection Using MCDM Techniques

35

operation research models which deal with decision problems in the presence of a number of decision criteria. This class is further divided into multi-objective decision-making (MODM) and multi-attribute decisionmaking (MADM). There are several methods in each of the above categories. Priority-based, outranking, distance-based and mixed methods are also applied to various problems. Each method has its own characteristics and such methods can also be classified as deterministic, stochastic and fuzzy methods (Pohekar and Ramachandran, 2004). In this paper we apply two MCDM methods, namely fuzzy AHP and fuzzy ANP, to select the best residential heating system alternative. This is the first study in the literature to apply these two techniques for heating systems. Analytic Hierarchy Process (AHP) is one of the common methods with which to solve MCDM problems. The first application to solve MCDM problem in the literature was Saaty’s choosing a school for his son using AHP (Saaty, 1980). The AHP is an approach that is suitable for dealing with complex systems related to make a choice among several alternatives with providing a comparison of the considered criteria and alternatives. AHP is based on the subdivision of the problem in a hierarchical form. By reducing complex decisions to a series of simple comparisons and rankings, then synthesizing the results, AHP not only helps the analysts to arrive at the best decision, but also provides a clear rationale for the choices made. The objective of using AHP is to identify the preferred alternative and also to determine a ranking of the alternatives when all the decision criteria are considered simultaneously (Mahmoodzadeh et al., 2007). The analytic network process (ANP) is also another common method with which to solve MCDM problems. The decision problem is structured hierarchically at different levels in the methodology (Mikhailov, 2003). The local priorities in ANP are established by means of pairwise comparisons and judgments (Promentilla et al., 2008). The analytical network process is a generalization of Saaty’s Analytical Hierarchy Process, which is one of the most widely employed decision support tools

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Yavuz Ozdemir and Sahika Ozdemir

(Promentilla et al., 2006). The priorities in the ANP are assessed indirectly by means of pairwise comparison judgments (Mikhailov and Singh, 2003). ANP is a useful tool for prediction and for representing a variety of competitors, their assumed interactions and their relative strengths to wield influence in making a decision (Tuzkaya et al., 2010). The rest of this paper is organized as follows: about energy consumption in housing, problem definition, fuzzy AHP methodology, fuzzy ANP methodology is given, respectively. Then, we show an application of fuzzy AHP methodology and fuzzy ANP methodology in evaluation of heating system alternatives. Computational results are given in this section. Finally, comparison of the results are discussed in last section, which concludes the paper.

ENERGY CONSUMPTION IN HOUSING Insulation of buildings in order to ensure energy efficiency, use of efficient heating and cooling systems, architectural evaluation of natural lighting applications, etc. applications can be counted. Air conditioning control is extremely important in modern buildings due to ventilation difficulties and the rapid increase of air pollution. With the introduction of technology into human life, the comfort and living standards of the individual have greatly increased. Thermal comfort is one of the most important comfort elements when considering continuous living areas. Although it differs from individual to individual, the thermal comfort requirement for the heating season is accepted as 50% relative humidity and indoor temperature ranging from 20 to 25°C. The buildings and buildings construction sectors combined are responsible for 36% of global final energy consumption and nearly 40% of total direct and indirect CO2 emissions. Energy demand from buildings and buildings construction continues to rise, driven by improved access to energy in developing countries, greater ownership and use of energyconsuming devices, and rapid growth in global buildings floor area, at nearly 3% per year.

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37

About 45% of energy use in the world is realized by the building sector. As energy is used for services to provide comfort conditions such as heating, cooling, ventilation in buildings, various levels of energy consumption are involved throughout the entire building life cycle, from raw material acquisition to demolition and destruction of the building. Therefore, significant reductions in energy consumption in the construction sector also contribute significantly to the reduction of total energy consumption.

Unvented Fuel-Fired Heaters In solid fuel systems used for heating or steam boiler applications, first of all, ignition must be carried out in the boiler. Ignition is carried out with preferred materials to ignite fuel. For this reason, boiler systems have a cooking zone. After the fire is lit in the furnace part, this continuously supported fire is fed with fuel at intervals. Boiler systems operating with solid fuel provide some basic advantages. As the central system is primarily preferred for central heating, heating can be performed more efficiently. So these systems provide both fast and efficient heating. Thanks to the steps such as insulation and protection, a very high energy saving can be achieved and easy to use. Maintenance and cleaning operations are carried out very easily and boilers have a very high safety during use.

HVAC Systems HVAC (Heating Ventilating and Air Conditioning) is a system that includes heating, ventilation and air conditioning and finds wide application areas such as factories, hospitals, shopping centers and places where energy consumption is high. Depending on the structure of the buildings, the energy consumed for HVAC constitutes between 15% and 60% of the total energy. Therefore, optimization of HVAC systems is very

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Yavuz Ozdemir and Sahika Ozdemir

important in terms of energy efficiency. HVAC systems provide energy efficiency and control. While providing comfort conditions at maximum level, it also balances moisture and bacteria rate in the air.

Electric Integrated Systems, Heat Pumps Fan Coil units are units that contain batteries as a fan and heat transfer surface. The heated air taken from the room with the help of a fan and passed over the batteries is blown back into the room. The water returning to the center by the return pipes is reheated and circulated here. For this purpose, circulation pumps are used. It has wide usage area such as commercial, social buildings and residences like hotel, office, store, restaurant, home. It offers safe, economical and practical solutions for heating. Since it is ready for installation from the factory, it can be used after electrical and installation connections are made. Low maintenance and repair costs increase the reason for preference. Floor and ceiling fan coils reduce the humidity rate by heating the air in apartments and villas. Fan Coil systems do not have ventilation, so they can only heat or cool.

Radiator Heating Systems In order to realize the use of heating, heat transfer is provided by radiators whether natural gas or central system heating is provided. Radiators produced by materials such as cast iron, steel or aluminum sheet panels are installed and made available in a specific installation scheme. The application realized at this point is called radiator installation application. The installation is achieved by connecting the radiators to be used to transfer the heat energy of the hot water coming from the natural gas or heating system. So radiator installation; radiators, valves and piping. In this installation, the hot water coming from the heat source shows circulation and thanks to this circulation the water temperature is transferred to the environment.

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39

Floor Heating Systems Underfloor heating is a system where comfort conditions can be fully achieved. Floor heating is used like all floor heating and heating can be provided at low temperatures. Low temperature means less energy consumption. Although the initial investment value is high, underfloor heating is guaranteed to have low energy consumption for life. Underfloor heating systems are suitable to work with all alternative energy types and thus lower costs. Another feature of underfloor heating systems is that they have a very long lifetime, which means that they are good for sustainability. It is also a useful system in terms of health, as it is used like all floor heater, it has the least air movement. Thus, dust circulation is very little compared to other systems.

MATERIALS AND METHODS Participants As we explained above, using energy effectively is crucial in today’s world. Prioritizing the residential heating system alternatives was chosen for this study and fuzzy AHP and fuzzy ANP approaches was used. We asked three sector experts (namely architect, civil engineer and mechanical engineer) with the same importance value about the problem of determining the best heating system alternative. Four main criteria, nineteen sub-criteria and five alternatives were determined and weighted accordingly. In the numerical example, the architect, the civil engineer and the mechanical engineer need to determine the best heating system alternative for a residential. For this problem, decision criteria and alternatives were defined by experts, as seen in Figure 1. In this paper the main criteria are environment, economic, physical attributes, and visuality. The arrows in Figure 1 represent the hierarchy of the problem.

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Yavuz Ozdemir and Sahika Ozdemir

Figure 1. Hierarchy of the problem.

Environment criteria (C1) include sub-criteria about environmental issues: “Energy Saving (C11),” “Ecologic (C12),” “Environmentally Friendly (C13),” “Green and Sustainable (C14),” “Continuousness (C15),” “Negative Impacts (C16),” and “Low CO2 Emmission (C17).” Economic criteria (C2) include sub-criteria about costs: “Installation Cost (C21),” “The Period of Use and Operation (C22),” “Maintenance Cost (C23),” and “Price Stabilization (C24)”. Physical Attributes criteria (C3) include the following sub-criteria: “Effective Usage (C31),” “Heating and Cooling Load (C32),” “Sizing (C33),” “Ability to Work in Low Temperatures (C34),” and “System Reliability (C35)”. Visuality criteria (C4) include the following sub-criteria: “The Purpose of Use of The Building (C41),” “Planning Module (C42),” and “Hidden Devices and Pipes (C43).” As seen in Figure 1 the alternatives for heating systems are “Unvented Fuel-Fired Heaters (A1),” “HVAC Systems (A2),” “Fan Coil Heating Systems (A3),” “Radiator Heating Systems (A4),” and “Floor Heating Systems (A5).”

FUZZY AHP METHODOLOGY Analytic Hierarchy Process (AHP) is one of the common methods with which to solve MCDM problems. The decision problem is structured

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hierarchically at different levels in this methodology (Mikhailov, 2003). The local priorities in AHP are established using pairwise comparisons and judgments (Promentilla et al., 2008). The priorities in the AHP are assessed indirectly from pairwise comparisons judgments (Mikhailov, 2003). AHP had been applied in a variety of contexts: from the simple everyday problem of selecting a school to the complex problems of designing alternative future outcomes of a developing country, evaluating political candidacy, allocating energy resources, and so on (Ozdagoglu and Ozdagoglu, 2007). To have a significant impact on the performance of the building with respect to the various design criteria, Nassar et al., (2003) developed a computer tool for selecting the best combination of building assemblies for each particular design situation. They used AHP to determine the relative importance weights for the different criteria. To select equipments for construction projects Shapira and Goldenberg (2005) presented a selection model based on AHP with a view to providing solutions for the systematic evaluation of soft factors, and the weighting of soft benefits in comparison with costs. Bitarafan et al., (2012) selected the appropriate method which can consider all the criteria of reconstructing the damaged areas that can be useful for decision makers in managing crises. They introduced the AHP method for calculating the relative importance of the criteria and their weights. The fuzzy AHP technique can be viewed as an advanced analytical method developed from the traditional AHP. The AHP’s subjective judgment, selection and preference of decision-makers have great influence on the success of the method. The conventional AHP still cannot reflect the human thinking style. Avoiding these risks on performance, the fuzzy AHP, an extension of AHP with fuzzy numbers, was developed to solve the hierarchical fuzzy problems. Buckley extended Saaty’s AHP to the case where the evaluators are allowed to employ fuzzy ratios in place of exact ratios to handle the difficulty for people to assign exact ratios when comparing two criteria and derive the fuzzy weights of criteria by geometric mean method (Hsieh et al., 2004).

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In the literature many researchers (Laarhoven and Pedrycz, 1983; Buckley, 1985a; Buckley, 1985b; Boender et al., 1989; Chang, 1996; Ribeiro, 1996; Lootsma, 1997) who had studied the fuzzy AHP, had provided evidence that fuzzy AHP shows relatively more sufficient description of these kind of decision making processes compared to the traditional AHP methods (Ozdagoglu and Ozdagoglu, 2007). Fuzzy AHP can be used for the evaluation and ranking of alternatives (Kahraman et al., 2004; Mikhailov and Tsvetinov, 2004; Rodríguez et al., 2013). Buyukozkan et al., (2008) proposed fuzzy AHP method to evaluate e-logistics-based strategic alliance partners. Cascales and Lamata (2008) proposed fuzzy AHP for management maintenance processes where only linguistic information was available. Alias et al., (2009) used F-AHP technique to rank alternatives to find the most reasonable and efficient use of river system. Zeng et al., (2007) presented a risk assessment methodology to cope with risks in complicated construction situations. A modified analytical hierarchy process with fuzzy numbers was used to structure and prioritize diverse risk factors. An illustrative example on risk analysis in a shopping centre was used to demonstrate their proposed methodology. Pan (2008) presented a fuzzy AHP model to select an appropriate bridge construction method. A case study involving an actual highway project was presented to illustrate the use of the proposed model in the paper. Also the use of the model and the capability of the model were shown with the results. Pan (2009) presented a fuzzy AHP approach to select an appropriate excavation construction method. A case study concerning a foundation construction project was presented in the paper. The author used Buckley’s fuzzy AHP approach to analyze the problem. Morote and Vila (2011) presented a risk assessment methodology based on the fuzzy sets theory and on the AHP. In this paper, a problem on risk assessment of a rehabilitation project of a building had been presented as a numerical example. Kog and Yaman (2014) analyzed 133 peer-reviewed academic studies that published between 1992 and 2013 and classified them as contractor

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selection, contractor pre-qualification and weighting criteria. According to their paper, the statistical models, fuzzy set theory, and AHP are the most preferred methods in order to solve contractor selection problem. Taylan et al., (2014) used analytic tools to evaluate the construction projects and their overall risks under incomplete and uncertain situations. They categorized the construction projects using fuzzy AHP and fuzzy TOPSIS methodologies. In their study Fuzzy AHP was used to create weights for fuzzy linguistic variable of construction projects overall risk. For the application thirty construction projects were studied with respect to five main criteria: time, cost, quality, safety and environment sustainability. Their results showed that these methodologies are able to assess the overall risks of construction projects, select the project that has the lowest risk with the contribution of relative importance index. Andric and Lu (2016) proposed a framework of disaster risk assessment combining Fuzzy Analytical Hierarchy Process (FAHP) with fuzzy knowledge representation and fuzzy logic techniques into a single integrated approach. They applied FAHP approach to ranking risk factors since it is more systematic, accurate and effective than traditional AHP. In the F-AHP and F-ANP, to evaluate the decision-makers’ preferences, pairwise comparisons are structured using triangular fuzzy numbers (al, am, au). The m x n fuzzy matrix can be given as in Eq. 1. The element AMN represents the comparison of the component m (row

~

element) with component n (column element). If A is a pairwise comparison matrix (Eq. 1), it is assumed that the reciprocal, and the reciprocal value, i.e., 1/amn, is assigned to the element amn (Tuzkaya and Onut, 2008; Tuzkaya et al., 2010):  (1,1,1)    1 1 1  , m, l  ~  u A    a11 a11 a11      1 1 1   au , am , al    1n 1n 1n 

a1ln , a1mn , a1un   l m u  (1,1,1)  a2n , a2n , a2n        1 1 1    u , m , l   (1,1,1)   a2n a2n a2n   a12l , a12m , a12u



(1)

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Yavuz Ozdemir and Sahika Ozdemir

Zadeh (1965) introduced the fuzzy set theory to deal with the uncertainty due to imprecision and vagueness. A major contribution of fuzzy set theory is its capability of representing vague data. A triangular fuzzy number that defined as (l,m,u), where l ≤ m ≤ u, denote the smallest possible value, the most promising value and the largest possible value. The steps of fuzzy AHP can be listed as follows (Hsieh et al., 2004; Kaya and Kahraman, 2011): Step 1: Determine alternatives, criteria and subcriteria to be used in the model Step 2: Create a hierarchy including goal, criteria, subcriteria, and alternatives. Step 3: Evaluate the relative importance of the criteria using pairwise comparisons. Assign linguistic terms to the pairwise comparisons by asking which criterion is more important than the other with fuzzy numbers.  1  Ã  ã 21     ãn1

ã12 1  ãn 2

 ã12  1 1 / ã 1   12     1 / ã1n 1 / ã2 n

 ... ã1n  ... ã 2 n        1 

(2)

  ã1n   ã2 n        1 

(3)

Step 4: Define the fuzzy geometric mean and fuzzy weight of each criterion. ~

r

i

 ( ãi1  ãi 2    ãin )1/ n ,

~

~

~

wi

(4)

~

 ri  (r1    rn ) 1

(5)

Residential Heating System Selection Using MCDM Techniques

45

where ãin is the fuzzy comparison value of criterion i to criterion n, thus, ~

ri

is the geometric mean of fuzzy comparison value of criterion i to each ~

criterion, wi is the fuzzy weight of the with criterion. Step 5: Defuzzify and normalize the fuzzy weights.

FUZZY ANP METHODOLOGY The analytic network process (ANP) is a generalization of the analytic hierarchy process (AHP) which can take the inner and outer dependencies among multiple criteria into consideration. ANP is used to determine the priorities of the elements in the network and the alternatives of the goal. ANP allows modeling complex and dynamic environments which are influenced by changing external factors (Meade and Sarkis, 1998). ANP is an excellent methodology which can deal with several issues by considering dependencies between nodes and clusters of criteria (Öztayşi et al., 2011). Buckley’s fuzzy AHP algorithm (Hsieh et al., 2004; Haghighi et al., 2010; Kaya and Kahraman, 2011) based fuzzy ANP is used for selecting the best heating system alternative in this paper. Fuzzy ANP allows measuring qualitative factors by using fuzzy numbers instead of crisp values in order to make decisions easier and obtain more realistic results (Öztayşi et al., 2013). In the literature, the fuzzy ANP method has been used to solve problems like research and development project selection (Mohanty et al., 2005), performance evaluation (Yellepeddi, 2006), quality function deployment implementation (Ertay et al., 2005), enterprise resource planning (ERP) software selection (Ayag and Ozdemir, 2007), tourism type prioritization (Demirel et al., 2010), etc. Öztayşi et al., (2011) compared the CRM performances of e-commerce firms using a multiple criteria decision making (MCDM) approach - ANP. A sensitivity analysis also provided in order to monitor the robustness of the proposed ANP framework to changes in the weights of evaluation

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Yavuz Ozdemir and Sahika Ozdemir

criteria. Their results showed that the ranking among the alternatives are sensitive to changes in the parameters. Tuzkaya et al., (2010) proposed an integrated fuzzy multi-criteria decision making methodology for selecting material handling equipment. The proposed approach utilizes fuzzy sets, ANP and the preference ranking organization method for enrichment evaluations (PROMETHEE). Tuzkaya and Onut (2008) proposed a model for selecting the most convenient transportation mode by considering the effects of criteria on the alternative modes and relations among the criteria clusters and subcriteria using fuzzy ANP. Buyukozkan et al., (2004) used fuzzy ANP to prioritize design requirements by taking into account the degree of the interdependence between customer needs and design requirements and their dependence. Ebrahimnejad et al., (2012) studied a construction project problem with multiple criteria in a fuzzy environment and proposed a new twophase group decision-making approach. This approach integrated a modified analytic network process (ANP) and an improved compromise ranking method, VIKOR. Zhou et al., (2013) proposed a flexibility measurement model of enterprise resources planning (ERP) based on a fuzzy analytic network process (FANP). Hung et al., (2012) applied the fuzzy analytic network process model to evaluate the strategic impact of new integrated circuit (IC) manufacturing technologies within Taiwan’s packaging industry. The steps of fuzzy ANP can be listed as follows (Yasmin et al., 2013): Step 1: Determine alternatives, criteria and subcriteria to be used in the model Step 2: Create a network including alternatives, criteria, subcriteria, inner and outer dependencies among the model. Step 3: Construct pairwise matrices of the components by the experts with fuzzy numbers. Step 4: Construct the fuzzy comparison matrix by using triangular fuzzy numbers:

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47

Step 5: Calculate fuzzy Eigen value to find whether the constructed matrix is consistent or not: To verify the consistency of the comparison matrix, Saaty proposed a consistency index (C.I.) and consistency ratio (C.R.). The consistency index of a matrix is given by C.I. = (λmax −n)/(n−1)

(6)

C.R = C.I/R.I

(7)

where, R.I is Random Consistency Index. The consistency index should be less than or equal to 0.10. Step 6: Forming initial supermatrix of the network of ANP is composed by listing all nodes horizontally and vertically. Step 7: Obtaining weighted supermatrix by multiplying the unweighted supermatrix with the corresponding cluster priorities Step 8: Calculating limited supermatrix by limiting the weighted supermatrix by raising it to sufficiently large power so that it converges into a stable supermatrix (i.e., all columns being identical).

APPLICATION: EVALUATION OF HEATING SYSTEM ALTERNATIVES In this paper we apply two MCDM methods to select the best heating system alternative, namely fuzzy AHP and fuzzy ANP. Then, the obtained results of these techniques are compared. The layout of the application case can be seen from Figure 2.

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Yavuz Ozdemir and Sahika Ozdemir

Figure 2. The layout of the application case.

Computational Results of Fuzzy AHP Methodology To solve the problem using fuzzy AHP, we used fuzzy numbers as shown in Table 1 and compared our results with those of experts. Evaluations of the alternatives by three experts with respect to the criteria can be seen in Table 2. The fuzzy weight matrix of the criteria according to the goal, fuzzy weight matrix of the subcriteria and fuzzy weight matrix of the alternatives with respect to each criterion are given in Tables 3, 4, and 5, respectively.

Residential Heating System Selection Using MCDM Techniques Table 1. Relationship between fuzzy numbers and degrees of linguistic importance Low/high Levels Label Linguistic Terms EL Extra low VL Very low L Low SL Slightly low M Middle SH Slightly high H High VH Very high EH Extra high

Triangular fuzzy numbers (1,1,1) (1,2,3) (2,3,4) (3,4,5) (4,5,6) (5,6,7) (6,7,8) (7,8,9) (9,9,9)

Table 2. Average values used in Fuzzy AHP and Fuzzy ANP Criteria C1 c11 c12 c13 c14 c15 c16 c17 C2 c21 c22 c23 c24 C3 c31 c32 c33 c34 c35 C4 c41 c42 c43

Importance value EH EH EH EH VH VH H VH H H H H H SH H H SH H H H SH H H

A1

A2

A3

A4

A5

VL VL VL VL VL EL EL

H H H H VH SL SL

SL SL L SL SL VL VL

M M SL SL M EL VL

SH SH SL SL M EL VL

M L M M

H SH SH H

SH SH M SH

H H SH H

VH H SH H

L SL SL L SL

H H SH SH H

SH H M M M

H H M SH H

H H H SH H

VL VL VL

SH SH SL

M M L

M SH L

SH H EH

49

50

Yavuz Ozdemir and Sahika Ozdemir Table 3. Fuzzy weight matrix of the criteria according to the goal

C1 C2 C3 C4

L 0.31 0.12 0.06 0.12

M 0.51 0.19 0.11 0.19

U 0.82 0.30 0.21 0.30

Table 4. Fuzzy weight matrix of the subcriteria

C11 C12 C13 C14 C15 C16 C17 C21 C22 C23 C24 C31 C32 C33 C34 C35 C41 C42 C43

L 0.11 0.11 0.11 0.06 0.06 0.03 0.06 0.25 0.25 0.25 0.25 0.17 0.17 0.07 0.17 0.17 0.12 0.26 0.26

M 0.21 0.21 0.21 0.11 0.11 0.06 0.11 0.25 0.25 0.25 0.25 0.22 0.22 0.11 0.22 0.22 0.20 0.40 0.40

U 0.35 0.35 0.35 0.21 0.21 0.13 0.21 0.25 0.25 0.25 0.25 0.28 0.28 0.23 0.28 0.28 0.40 0.58 0.58

Then, the fuzzy weights defuzzified and normalized. The evaluation and the methodology described above produced the results shown in Table 6. According to the results in Table 6 the ranking is obtained as A5>A2>A4>A3>A1.

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Table 5. Fuzzy weight matrix of the alternatives with respect to each criterion

A1 A2 A3 A4 A5 A1 A2 A3 A4 A5 A1 A2 A3 A4 A5 A1 A2 A3 A4 A5

L C11 0.03 0.23 0.06 0.09 0.14 C16 0.08 0.29 0.10 0.08 0.08 C24 0.05 0.17 0.08 0.17 0.17 C35 0.04 0.21 0.06 0.21 0.21

M

U

0.05 0.42 0.10 0.16 0.27

0.08 0.71 0.20 0.32 0.50

0.11 0.47 0.20 0.11 0.11

0.17 0.73 0.36 0.17 0.17

0.08 0.26 0.14 0.26 0.26

0.15 0.38 0.29 0.38 0.38

0.06 0.28 0.10 0.28 0.28

0.10 0.36 0.16 0.36 0.36

L C12 0.03 0.23 0.06 0.09 0.14 C17 0.05 0.26 0.10 0.10 0.10 C31 0.03 0.19 0.09 0.19 0.19 C41 0.03 0.17 0.11 0.11 0.17

M

U

0.05 0.42 0.10 0.16 0.27

0.08 0.71 0.20 0.32 0.50

0.09 0.44 0.16 0.16 0.16

0.18 0.70 0.24 0.24 0.24

0.05 0.27 0.15 0.27 0.27

0.07 0.37 0.28 0.37 0.37

0.05 0.31 0.17 0.17 0.31

0.08 0.49 0.31 0.31 0.49

L C13 0.04 0.35 0.06 0.10 0.10 C21 0.04 0.12 0.06 0.12 0.18 C32 0.05 0.21 0.21 0.21 0.21 C42 0.03 0.13 0.07 0.13 0.20

M

U

0.06 0.52 0.10 0.16 0.16

0.10 0.77 0.18 0.25 0.25

0.07 0.22 0.12 0.22 0.37

0.15 0.40 0.26 0.40 0.69

0.06 0.24 0.24 0.24 0.24

0.08 0.26 0.26 0.26 0.26

0.04 0.22 0.13 0.22 0.38

0.07 0.39 0.26 0.39 0.66

L C14 0.04 0.35 0.11 0.11 0.11 C22 0.03 0.11 0.11 0.17 0.17 C33 0.04 0.12 0.08 0.08 0.21 C43 0.03 0.09 0.06 0.06 0.46

M

U

0.06 0.51 0.15 0.15 0.15

0.09 0.71 0.19 0.19 0.19

0.05 0.17 0.17 0.31 0.31

0.08 0.31 0.31 0.49 0.49

0.08 0.24 0.14 0.14 0.40

0.17 0.50 0.26 0.26 0.72

0.05 0.15 0.09 0.09 0.62

0.09 0.23 0.14 0.14 0.83

L C15 0.03 0.31 0.06 0.12 0.12 C23 0.08 0.15 0.08 0.15 0.15 C34 0.04 0.18 0.09 0.18 0.18

M

U

0.05 0.48 0.10 0.18 0.18

0.08 0.73 0.19 0.29 0.29

0.13 0.25 0.13 0.25 0.25

0.25 0.38 0.25 0.38 0.38

0.06 0.27 0.14 0.27 0.27

0.10 0.37 0.29 0.37 0.37

Table 6. Results of the application using Fuzzy AHP

A1 A2 A3 A4 A5

Unvented Fuel-Fired Heaters HVAC Systems Fan Coil Heating Systems Radiator Heating Systems Floor Heating Systems

Weights 0.3301 1.4711 0.7119 1.0028 1.5100

Normalized Values 6.57% 29.27% 14.17% 19.95% 30.04%

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Yavuz Ozdemir and Sahika Ozdemir

Computational Results of Fuzzy ANP Methodology To solve the problem using fuzzy ANP, we used fuzzy numbers as shown in Table 1 and compared our results with those of experts. Evaluations of the alternatives by three experts with respect to the criteria, fuzzy weight matrix of the criteria according to the goal, fuzzy weight matrix of the subcriteria and fuzzy weight matrix of the alternatives with respect to each criterion were the same as the values of the fuzzy AHP that can be seen in Table 2-5. Due to the formulation of the ANP methodology, the subcriteria comparisons are made between each other as can be seen in Table 7. Table 7. The fuzzy comparison matrix of subcriteria

C11 C12 C13 C14 C15 C16 C17 C21 C22 C23 C24 C31 C32 C33 C34 C35 C41 C42 C43

L 0.06 0.06 0.06 0.03 0.03 0.02 0.03 0.02 0.02 0.02 0.02 0.02 0.02 0.01 0.02 0.02 0.01 0.02 0.02

M 0.11 0.11 0.11 0.07 0.07 0.04 0.07 0.04 0.04 0.04 0.04 0.04 0.04 0.02 0.04 0.04 0.02 0.04 0.04

U 0.20 0.20 0.20 0.14 0.14 0.06 0.14 0.06 0.06 0.06 0.06 0.06 0.06 0.05 0.06 0.06 0.05 0.06 0.06

Also initial supermatrix, weighted supermatrix and the limited supermatrix can be seen from Table 8-10. The evaluation and the methodology described above produced the results shown in Table 11.

Table 8. Initial supermatrix

C11 C12 C13 C14 C15 C16 C17 C21 C22 C23 C24 C31 C32 C33 C34 C35 C41 C42 C43

C11 0.195 0.195 0.195 0.115 0.115 0.071 0.115 0.250 0.250 0.250 0.250 0.220 0.220 0.140 0.220 0.200 0.226 0.387 0.387

C12 0.195 0.195 0.195 0.115 0.115 0.071 0.115 0.250 0.250 0.250 0.250 0.216 0.216 0.137 0.216 0.216 0.226 0.387 0.387

C13 0.195 0.195 0.195 0.115 0.115 0.071 0.115 0.250 0.250 0.250 0.250 0.216 0.216 0.137 0.216 0.216 0.226 0.387 0.387

C14 0.195 0.195 0.195 0.115 0.115 0.071 0.115 0.250 0.250 0.250 0.250 0.216 0.216 0.137 0.216 0.216 0.226 0.387 0.387

C15 0.195 0.195 0.195 0.115 0.115 0.071 0.115 0.250 0.250 0.250 0.250 0.216 0.216 0.137 0.216 0.216 0.226 0.387 0.387

C16 0.195 0.195 0.195 0.115 0.115 0.071 0.115 0.250 0.250 0.250 0.250 0.216 0.216 0.137 0.216 0.216 0.212 0.394 0.394

C17 0.195 0.195 0.195 0.115 0.115 0.071 0.115 0.250 0.250 0.250 0.250 0.216 0.216 0.137 0.216 0.216 0.226 0.387 0.387

C21 0.195 0.195 0.195 0.115 0.115 0.071 0.115 0.250 0.250 0.250 0.250 0.216 0.216 0.137 0.216 0.216 0.226 0.387 0.387

C22 0.195 0.195 0.195 0.115 0.115 0.071 0.115 0.250 0.250 0.250 0.250 0.216 0.216 0.137 0.216 0.216 0.226 0.387 0.387

C23 0.195 0.195 0.195 0.115 0.115 0.071 0.115 0.250 0.250 0.250 0.250 0.216 0.216 0.137 0.216 0.216 0.226 0.387 0.387

C24 0.195 0.195 0.195 0.115 0.115 0.071 0.115 0.250 0.250 0.250 0.250 0.216 0.216 0.137 0.216 0.216 0.226 0.387 0.387

C31 0.195 0.195 0.195 0.115 0.115 0.071 0.115 0.250 0.250 0.250 0.250 0.216 0.216 0.137 0.216 0.216 0.226 0.387 0.387

C32 0.195 0.195 0.195 0.115 0.115 0.071 0.115 0.250 0.250 0.250 0.250 0.216 0.216 0.137 0.216 0.216 0.226 0.387 0.387

C33 0.195 0.195 0.195 0.115 0.115 0.071 0.115 0.250 0.250 0.250 0.250 0.216 0.216 0.137 0.216 0.216 0.226 0.387 0.387

C34 0.195 0.195 0.195 0.115 0.115 0.071 0.115 0.250 0.250 0.250 0.250 0.216 0.216 0.137 0.216 0.216 0.226 0.387 0.387

C35 0.195 0.195 0.195 0.115 0.115 0.071 0.115 0.250 0.250 0.250 0.250 0.216 0.216 0.137 0.216 0.216 0.226 0.387 0.387

C41 0.195 0.195 0.195 0.115 0.115 0.071 0.115 0.250 0.250 0.250 0.250 0.216 0.216 0.137 0.216 0.216 0.226 0.387 0.387

C42 0.195 0.195 0.195 0.115 0.115 0.071 0.115 0.250 0.250 0.250 0.250 0.216 0.216 0.137 0.216 0.216 0.226 0.387 0.387

C43 0.195 0.195 0.195 0.115 0.115 0.071 0.115 0.250 0.250 0.250 0.250 0.216 0.216 0.137 0.216 0.216 0.226 0.387 0.387

Table 9. Weighted supermatrix

C11 C12 C13 C14 C15 C16 C17 C21 C22 C23 C24 C31 C32 C33 C34 C35 C41 C42 C43

C11 0.099 0.099 0.099 0.059 0.059 0.036 0.059 0.046 0.046 0.046 0.046 0.026 0.026 0.017 0.026 0.024 0.042 0.072 0.072

C12 0.099 0.099 0.099 0.059 0.059 0.036 0.059 0.046 0.046 0.046 0.046 0.026 0.026 0.017 0.026 0.026 0.042 0.072 0.072

C13 0.099 0.099 0.099 0.059 0.059 0.036 0.059 0.046 0.046 0.046 0.046 0.026 0.026 0.017 0.026 0.026 0.042 0.072 0.072

C14 0.099 0.099 0.099 0.059 0.059 0.036 0.059 0.046 0.046 0.046 0.046 0.026 0.026 0.017 0.026 0.026 0.042 0.072 0.072

C15 0.099 0.099 0.099 0.059 0.059 0.036 0.059 0.046 0.046 0.046 0.046 0.026 0.026 0.017 0.026 0.026 0.042 0.072 0.072

C16 0.099 0.099 0.099 0.059 0.059 0.036 0.059 0.046 0.046 0.046 0.046 0.026 0.026 0.017 0.026 0.026 0.039 0.073 0.073

C17 0.099 0.099 0.099 0.059 0.059 0.036 0.059 0.046 0.046 0.046 0.046 0.026 0.026 0.017 0.026 0.026 0.042 0.072 0.072

C21 0.099 0.099 0.099 0.059 0.059 0.036 0.059 0.046 0.046 0.046 0.046 0.026 0.026 0.017 0.026 0.026 0.042 0.072 0.072

C22 0.099 0.099 0.099 0.059 0.059 0.036 0.059 0.046 0.046 0.046 0.046 0.026 0.026 0.017 0.026 0.026 0.042 0.072 0.072

C23 0.099 0.099 0.099 0.059 0.059 0.036 0.059 0.046 0.046 0.046 0.046 0.026 0.026 0.017 0.026 0.026 0.042 0.072 0.072

C24 0.099 0.099 0.099 0.059 0.059 0.036 0.059 0.046 0.046 0.046 0.046 0.026 0.026 0.017 0.026 0.026 0.042 0.072 0.072

C31 0.099 0.099 0.099 0.059 0.059 0.036 0.059 0.046 0.046 0.046 0.046 0.026 0.026 0.017 0.026 0.026 0.042 0.072 0.072

C32 0.099 0.099 0.099 0.059 0.059 0.036 0.059 0.046 0.046 0.046 0.046 0.026 0.026 0.017 0.026 0.026 0.042 0.072 0.072

C33 0.099 0.099 0.099 0.059 0.059 0.036 0.059 0.046 0.046 0.046 0.046 0.026 0.026 0.017 0.026 0.026 0.042 0.072 0.072

C34 0.099 0.099 0.099 0.059 0.059 0.036 0.059 0.046 0.046 0.046 0.046 0.026 0.026 0.017 0.026 0.026 0.042 0.072 0.072

C35 0.099 0.099 0.099 0.059 0.059 0.036 0.059 0.046 0.046 0.046 0.046 0.026 0.026 0.017 0.026 0.026 0.042 0.072 0.072

C41 0.099 0.099 0.099 0.059 0.059 0.036 0.059 0.046 0.046 0.046 0.046 0.026 0.026 0.017 0.026 0.026 0.042 0.072 0.072

C42 0.099 0.099 0.099 0.059 0.059 0.036 0.059 0.046 0.046 0.046 0.046 0.026 0.026 0.017 0.026 0.026 0.042 0.072 0.072

C43 0.099 0.099 0.099 0.059 0.059 0.036 0.059 0.046 0.046 0.046 0.046 0.026 0.026 0.017 0.026 0.026 0.042 0.072 0.072

Table 10. Limited supermatrix

C11 C12 C13 C14 C15 C16 C17 C21 C22 C23 C24 C31 C32 C33 C34 C35 C41 C42 C43

C11 0.099 0.099 0.099 0.059 0.059 0.036 0.059 0.046 0.046 0.046 0.046 0.026 0.026 0.017 0.026 0.026 0.042 0.072 0.072

C12 0.099 0.099 0.099 0.059 0.059 0.036 0.059 0.046 0.046 0.046 0.046 0.026 0.026 0.017 0.026 0.026 0.042 0.072 0.072

C13 0.099 0.099 0.099 0.059 0.059 0.036 0.059 0.046 0.046 0.046 0.046 0.026 0.026 0.017 0.026 0.026 0.042 0.072 0.072

C14 0.099 0.099 0.099 0.059 0.059 0.036 0.059 0.046 0.046 0.046 0.046 0.026 0.026 0.017 0.026 0.026 0.042 0.072 0.072

C15 0.099 0.099 0.099 0.059 0.059 0.036 0.059 0.046 0.046 0.046 0.046 0.026 0.026 0.017 0.026 0.026 0.042 0.072 0.072

C16 0.099 0.099 0.099 0.059 0.059 0.036 0.059 0.046 0.046 0.046 0.046 0.026 0.026 0.017 0.026 0.026 0.042 0.072 0.072

C17 0.099 0.099 0.099 0.059 0.059 0.036 0.059 0.046 0.046 0.046 0.046 0.026 0.026 0.017 0.026 0.026 0.042 0.072 0.072

C21 0.099 0.099 0.099 0.059 0.059 0.036 0.059 0.046 0.046 0.046 0.046 0.026 0.026 0.017 0.026 0.026 0.042 0.072 0.072

C22 0.099 0.099 0.099 0.059 0.059 0.036 0.059 0.046 0.046 0.046 0.046 0.026 0.026 0.017 0.026 0.026 0.042 0.072 0.072

C23 0.099 0.099 0.099 0.059 0.059 0.036 0.059 0.046 0.046 0.046 0.046 0.026 0.026 0.017 0.026 0.026 0.042 0.072 0.072

C24 0.099 0.099 0.099 0.059 0.059 0.036 0.059 0.046 0.046 0.046 0.046 0.026 0.026 0.017 0.026 0.026 0.042 0.072 0.072

C31 0.099 0.099 0.099 0.059 0.059 0.036 0.059 0.046 0.046 0.046 0.046 0.026 0.026 0.017 0.026 0.026 0.042 0.072 0.072

C32 0.099 0.099 0.099 0.059 0.059 0.036 0.059 0.046 0.046 0.046 0.046 0.026 0.026 0.017 0.026 0.026 0.042 0.072 0.072

C33 0.099 0.099 0.099 0.059 0.059 0.036 0.059 0.046 0.046 0.046 0.046 0.026 0.026 0.017 0.026 0.026 0.042 0.072 0.072

C34 0.099 0.099 0.099 0.059 0.059 0.036 0.059 0.046 0.046 0.046 0.046 0.026 0.026 0.017 0.026 0.026 0.042 0.072 0.072

C35 0.099 0.099 0.099 0.059 0.059 0.036 0.059 0.046 0.046 0.046 0.046 0.026 0.026 0.017 0.026 0.026 0.042 0.072 0.072

C41 0.099 0.099 0.099 0.059 0.059 0.036 0.059 0.046 0.046 0.046 0.046 0.026 0.026 0.017 0.026 0.026 0.042 0.072 0.072

C42 0.099 0.099 0.099 0.059 0.059 0.036 0.059 0.046 0.046 0.046 0.046 0.026 0.026 0.017 0.026 0.026 0.042 0.072 0.072

C43 0.099 0.099 0.099 0.059 0.059 0.036 0.059 0.046 0.046 0.046 0.046 0.026 0.026 0.017 0.026 0.026 0.042 0.072 0.072

56

Yavuz Ozdemir and Sahika Ozdemir Table 11. Results of the application using Fuzzy ANP

A1 A2 A3 A4 A5

Weights 0.0936 0.5114 0.1940 0.2657 0.3547

Unvented Fuel-Fired Heaters HVAC Systems Fan Coil Heating Systems Radiator Heating Systems Floor Heating Systems

Normalized Values 6.59% 36.03% 13.67% 18.72% 24.99%

According to the results in Table 11 the ranking is obtained as A2>A5>A4>A3>A1. The impact of interactivity among main criteria and subcriteria in fuzzy ANP are the reason of the variations in the weights, normalized values, and also in the ranking. When interactions among main criteria and subcriteria taken into consideration in the methodology, fuzzy ANP methodology is better than fuzzy AHP for daily usage. Given these results, it is fair to say that selecting Alternative A2 (HVAC Systems) is the most reasonable outcome, followed by the others (Table 12). Table 12. Comparison of the results using Fuzzy AHP and Fuzzy ANP Alternatives A1 Unvented Fuel-Fired Heaters A2 HVAC Systems A3 Fan Coil Heating Systems A4 Radiator Heating Systems A5 Floor Heating Systems

Weights Normalized Values Ranking Fuzzy AHP Fuzzy ANP Fuzzy AHP Fuzzy ANP Fuzzy AHP Fuzzy ANP 0.3301 0.0936 6.57% 6.59% 5 5

1.4711

0.5114

29.27%

36.03%

2

1

0.7119

0.1940

14.17%

13.67%

4

4

1.0028

0.2657

19.95%

18.72%

3

3

1.5100

0.3547

30.04%

24.99%

1

2

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CONCLUSION In this paper, multi criteria-decision making techniques, fuzzy AHP and fuzzy ANP methods are used for the evaluation of residential heating system alternatives. Then, the obtained results of these techniques are compared. As a result of evaluation process, fuzzy AHP has determined the most suitable result as A5 (Floor Heating Systems, 30.04%); on the other hand, fuzzy ANP has determined the most suitable result as A2 (HVAC Systems, 36.03%). In Fuzzy AHP results, the normalized values of A5 and A2 are 30.04% and 29.27% with so close weights. But in fuzzy ANP results, A2 has opened the gap with the alternative A5. The normalized values of A2 and A5 are 36.03% and 24.99%. The ranking of other alternatives are A4 > A3 > A1 in both methods. The main purpose of HVAC systems installed in buildings is to fulfill the comfort conditions at optimum temperatures by increasing the air quality in the environments where people are present. HVAC systems activate the ventilation systems at an appropriate level by taking the appropriate temperature, pressure and humidity data from the environments in which they are installed. The most suitable control systems that can be provided in HVAC systems is to control the pressure and temperature with automatic control. After this control, the system reaches the desired values by using a limited energy because the most important feature of the system is to reach the maximum efficiency with minimum energy and provide the necessary conditions. Fuzzy ANP methodology considers interactivity among main criteria and subcriteria. When interactions among criteria exist, fuzzy ANP is proved to be an adequate aggregation operator by taking into account the interactions. In addition, the proposed methodology has an ability of evaluating heating system information from internal and external environments. The main advantage of the proposed model is to indicate the impact of this interactivity using triangular fuzzy numbers. The main contribution of this paper is to determine the interactivity among main

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criteria and subcriteria. When interactivity among main criteria and subcriteria consists, fuzzy ANP gives better results. The general limitation of the proposed model is the costly and exhausting information requested from experts (approx. 360 pairwise comparisons per one expert). Other limitations of the model are the preferences of the expert including uncertainty and conflicts and there is often needed more than one expert to make decisions. It is more likely to get subjective results especially with evaluations made with a single expert. However, in this study, we asked three sector experts (namely architect, civil engineer and mechanical engineer) with the same importance value about the problem of determining the best heating system alternative.

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In: Heating Systems Editor: Elias Moore

ISBN: 978-1-53617-557-8 © 2020 Nova Science Publishers, Inc.

Chapter 3

DESIGNING A LOGISTICS SYSTEM TO ENSURE EFFICIENT DISTRIBUTION OF LPG ENERGY Yavuz Ozdemir*, PhD Faculty of Engineering and Natural Sciences, Istanbul Sabahattin Zaim University, Istanbul, Turkey

ABSTRACT LPG (liquefied petroleum gas) is a fuel-efficient, high-heat valuable and scarce resource. This research is aimed at designing a logistics system for X Gas Company to ensure efficient distribution of LPG, which begins with the ordering process and ends with the placement of stations in Istanbul-Turkey, taking into account the storage, preparation, loading and delivery operations of X Gas Company. Because gas as energy is a scarce resource, the primary performance indicators are distribution, time, cost, and stocklessness. Thus, the ultimate goal is to increase retailer and consumer satisfaction. The aim of this study is to develop a systematic approach using engineering tools and optimization software.

*

Corresponding Author’s E-mail: [email protected].

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Yavuz Ozdemir One of the biggest problems in this study is the “Vehicle Routing Problem”. The optimal route was found using an optimization software for the Vehicle Routing Problem and the routing costs were reduced. After the design of the routing system, a decision support system has been submitted in order to respond to future changes related to the current system. Finally, the designed program was run for different scenarios and the results were analyzed. In light of these studies, it has been proven that a high amount of saving improvement is possible within the current system.

Keywords: vehicle routing problem, gas distribution, logistics, LPG (liquefied petroleum gas), optimization

INTRODUCTION Logistics can be defined as, depending on time resources (products, labor, production capacity, information...) in the right place, at the right time, with the right quality, having the right price in a wide sense [1]. By the right price, we mean to provide the desired product or service within the framework of international competition conditions at a reasonable and acceptable cost, i.e., at a competitive price. In a narrow sense, logistics can be defined as strategic management of the total supply chain. According to the definition of the Council of Logistics Management (CLM), logistics is the process of ensuring, controlling and planning the flow and storage of all kinds of products, services and information to meet customers’ needs, the movement of raw materials from the starting point to the end point where the product is consumed, and the inventory in the process [2]. When the concept of logistics is used for foreign trade, the meaning of the concept is limited. In this case, logistics is expressed as the management of all the activities that enable the transportation of a final product from the production place in the exporting country to the point of consumption in the importing country in order to meet the needs of the customers for a certain price [3]. The competitive environment that companies have entered into together with the globalized economy has led companies to withdraw from

Designing a Logistics System to Ensure Efficient Distribution …

67

many points in their production processes and transfer these stages to other companies that they can control in terms of cost, time and quality. The companies receiving this service are able to compete in terms of speed while reducing their costs. Thus, the companies that were previously in transportation, began to provide services far beyond transportation. In addition, more companies are becoming involved in this environment by means of exports, joint ventures, acquisitions or license transfers to international markets. This trend will continue in the future in parallel with the development of international trade. Spreading to international markets brings with it the need for a logistics network spread across the world. From this perspective, the operation of the international logistics system will require more than an activity to be developed within the country, in particular the capability, knowledge and capacity of international finance, certification, political knowledge and foreign trade practices and customs of foreign countries [4]. In today’s global economy, the concept of logistics, which is the rising value in terms of competition, appears to be an indispensable requirement to examine its relationship with shipping. It is certain that there are numerous benefits in reducing costs and providing better competition for customers with logistics services. Businesses that fail to act as a team in the sense of team spirit in the face of recent customers will not have a chance to succeed. The end customers will reach the peak with the adoption of the concepts of productivity growth and cost reduction in each unit and staff, from production to transportation, from storage to after-sales services, ensuring the maximum customer satisfaction. Some of the opportunities and pursuits created by global trade trends have shown their impact in international logistics, customer demands and needs have diversified and increased, and the approach of logistics management from the understanding based on transportation has started to come to the fore [5]. In broad terms, the duty of logistics is to ensure the delivery of products, raw materials and auxiliary materials when needed, under appropriate conditions, at the lowest cost where needed [6].

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Logistics means many tools (stock management, order processing, storage, site selection, traffic selection, order processing, warehousing, location selection and management, route management, handling, forecasting, transportation, protective packaging, etc.) and requiring very serious integrated scientific preparations and strategies [7]. According to Ricardo Ernst, “the global conditions we are in have further increased the importance of logistics processes between the production and purchase of a product, which directly affect the profitability of the company. In the past, many companies had developed strategies focused on marketing, finance and production. This approach was due to lack of awareness of the process that developed between the production and purchase of the product. Today, companies aim to increase their cash flows and profitability by applying the right logistics processes instead of reducing costs during the production phase” [8]. No business process involves the complexity and geographic length of logistics. Logistics is the subject of twenty-four hours a day, seven days a week, fifty-two weeks of the year, and the availability of products and services when they are needed all over the world. Without logistics, it is difficult to succeed in marketing, production and international trade. Logistics competence is of great importance in developed industrial societies; consumers expect the products they buy to be delivered as promised [9]. Waters explained the scope of logistics as follows: A logistics is the function responsible for the transfer of materials from a supplier to customers through transactions within the organization. Material transfer from suppliers to the organization inbound or inward logistics; the transfer of materials to customers is called outbound or outward logistics, and the movement of materials within the organization is called material management [10]. The Vehicle Routing Problem (VRP) was first introduced to the literature by Dantzig and Ramser in 1959. In this study, the authors focused on the problem of distribution of gasoline to stations and established the first mathematical programming model for solving the problem [11]. Later in 1964, Clark and Wright proposed an intuitive

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solution to the problem, but after this study, interest in VRP grew further in the literature. VRP is one of the optimization problems on which the most methods have been developed so far [12]. Solomon has studied several problems concerning time constraints on VRP. In this type of problem, the spatial understanding of scheduling is confused with the temporal understanding. Additionally, Koskosidis has allowed for more flexible runtime constraints. According to Koskosidis, if the vehicle reaches too late or too early, this requires a penalty cost. Malakandri and Daskin have worked on traffic congestion-based, time-dependent VRP algorithms. As a result, they suggested that there would be no fixed route in the event of traffic jams, and that the time to travel would depend on the time of day [13]. In the 1980s, the fact that processor speeds were far lower than those of today kept researchers away from VRPs, which were more complex. However, beginning in the 1990s and extending to the present day, many different studies have been done on the VRP. Researchers have proposed precise solution methods, heuristics or meta-heuristics, in order to find optimal or near-optimal solutions [14]. VRP is divided into various groups according to different parameters and domains of these parameters. The objective function of a vehicle routing problem may be to minimize the total distance or time traveled, or to minimize the number of vehicles. VRP with the most general perspective are divided into various groups according to whether the number of vehicles to be one or more, the vehicle fleet to be the same type or different types, the number of warehouses to be one or more, customer demands to be deterministic or stochastic, or to be some constraints as total time or total distance, vehicle capacity or time window, etc. Finding optimal solutions for VRP is becoming more difficult. This is due to increasing competition conditions and the addition of more restrictions to routing problems due to changing environmental conditions. Time intervals (arrival time, service time, standby time and departure time), a large number of vehicles with different capacities, travel time allowed on the route, different speeds between different points, rest times for vehicle drivers are the constraints that should be considered in vehicle

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routing. The increase of the constraints used in the routing, makes the problem of vehicle routing more and more complicated [15]. The vehicle routing problem is an NP-hard combinatorial optimization problem that occurs during routing in the distribution of enterprises, which significantly determines the costs in this scope, and where sorting and grouping are performed. The solution methods of such problems are divided into two groups: exact and approximate methods [12]. Although the exact methods give optimum results, they can only be applied to small-sized problems. These problems take a long time to solve because there is a lot of processing requirements [12]. As the problem becomes NP-hard and the problem size increases and the time of solution increases exponentially, the work done for the solution is more focused on approximate methods [16]. Approximate methods produce near-optimal, good quality solutions for large-scale problems with less processing and short computing time. In the rest of this study logistics, logistics costs, hazardous materials logistics, Vehicle Routing Problems are presented and a real application for LPG distribution as a Vehicle Routing Problems is discussed. The mathematical model for this real problem is established to determine the most appropriate route for fuel company from its warehouse to 15 stations in Istanbul-Turkey, in order to meet the demands with 10 vehicles of heterogeneous fleet type. Linear programming as an exact method is used to achieve an optimal result for the problem. For finding an optimal solution to the VRP, vehicles start the route from the warehouse, visit customers and return to the warehouse again. The number of routes within the network is equal to the number of vehicles used. A vehicle can only operate on one route. Each customer within the network can be visited by only one of the vehicles. All vehicles have the different capacity and customer demands can vary. The capacity of the vehicle must be less than or equal to the sum of the demands of all customers on the route. This means that the vehicles cannot be loaded above their capacity. After the computational results that gained using a optimization software are shown, the conclusion is given.

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LOGISTICS Initially, logistics activities covering only transport and storage services have diversified over time to include more space as a result of the expansion of the scope and area of logistics. Today, logistics, other than material transportation and storage; inventory management, packaging, material handling (transport), order processing (order process), forecasting, production planning, purchasing, customer service, parts, and service support, returning materials, transportation, recovery and destruction of wastes, factories and warehouses, and to cover activities such as determination of the place of establishment of communication has gained a wider dimension [17]. Logistics has its inherent elements of forecasting, planning, organizing, organization, coordination and control. Logistics, a product or service in the production and distribution of all activities in relation to the transport and management. The aim of logistics is to make the organization resistant to vital market variables such as quality, price time and service in order to sustain the company’s existence [18]. The most prominent activities of logistics are demand management, inventory management, customer service, order processing, storage, handling, and transportation. Efficient demand management provides the power and flexibility to meet the product demanded by the customer in the desired quantity, quality and variety, at the right price and at the right place. Therefore, demand management aims to ensure that demand is met at the maximum level depending on the information, whereas delay time, costs and inventories are minimized. Effective demand management requires information communication. In order to manage the relations well and to meet the demand effectively, it is necessary to have flexible production skills in addition to healthy demand forecasting. In short, flexible production and demand forecasting are the main two inputs of effective demand management [19]. Inventory management is the possession of materials, semi-processed and completed products in order to keep production at the desired level and

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to perform delivery and sales according to the desired specifications [19]. In inventory management, international submissions have a different effect than in-country movements. Some products are easy to move internationally, while others are more difficult or costly. For example, international transport is often very difficult due to health issues in live animals. In addition, if the cargo will pass through customs, it is important that the cargo is controllable. It would be easier for the controllable cargo to come out of customs [20]. Due to the very small materials and parts that are not available in time, the entire production system can be blocked, as well as the existing customer potential can be lost; however, the cost of the input items in the stock that are not included in the production line at that time can be large due to the lack of proper planning of the demand. Therefore, with the right logistics strategies, the operating costs resulting from inventory can be reduced significantly at this point [2]. In particular, the growth of the production systems of enterprises and the increase of product variety, the uncertainties in supply, demand and product-related factors and the complexity of the relationship between them have made significant applications to keep inventory at a sufficient level [21]. When the development process of customer service management in recent years are examined, the big change of the business world can be seen. In the world, it is observed that competition in almost all markets is increasing rapidly, the time of products entering the market is getting shorter and profit margins are falling. In such an environment, it is important to reach the customer more effectively, understand the customer’s demands and shape the products and services in this direction. Service support, settlement, evaluation of returned goods, recovery of goods and evaluation of customer complaints and demands are among the support services in the logistics workflow [2]. The aim of customer service in logistics management is to do everything right the first time. The essence of this is to increase the logistic performance within the scope of total quality understanding in the whole system which is assumed by the customer, marketing philosophy, processes and materials very well

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defined. For this, the management’s point of view to the customer is very important. The transactions, behaviors and documentation performed within the period from receipt of the order to delivery are positioned as part of the service in the mind of the customer [19]. The critical point in logistics business processes is the delivery of customer orders on-site and on time with a result that will satisfy the customer. Therefore, it is important to manage this process with the most accurate techniques and methods. At this point, information management should be considered priority [2]. Most logistic functions require storage, processing, holding large amounts of data, real-time communication capacities, easy-to-use or complex analytical tools, and report manufacturers. Some new developments in information technologies meet all the requirements of logistics functions [22]. Distribution centers are one of the most important rings in the supply chain and one of the most important points in the realization of physical distribution. Warehouses are places within the enterprise where raw materials, semi-finished and finished products are kept [2]. Storage is the delivery of goods/cargo from certain points to be taken, handled or preserved for a certain period of time and sent to certain points [20]. Providing a place of sufficient size and quality for the storage and protection of inventories is the leading requirement in logistics and inventory control. It is important that the desired part is immediately available in the warehouse and can be easily transported to the place of need. Coding, division of the storage volumes according to their coordinates, division according to the frequency of use, placement of the frequently used parts to keep the transport distances short, warehouse building structure, ground quality, easy movement of vehicles, fire, security, simple but effective recording systems these are the issues that need to be carefully considered [23]. Storage, transport and inventory control are very closely linked logistical elements. The mode of transport and the number of vehicles used closely affect the availability of goods in the inventory. If a slow mode of transport is preferred, the inventory level should be kept high and large-scale warehouses should be used. If the

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number of warehouses used is low and small scale, a fast mode of transport should be preferred. Temporary storage of goods in the same situation provided that the appearance and technical characteristics of the protection of certain transactions under the permission and supervision of the customs administration can be subject to change. These operations are called handling. The handling process is carried out during the transportation, storage and loading of the products and this process directly affects the efficiency of the processes. It is a process that does not change the value of the product, does not provide added value, but causes a loss in the value of the product if it is not done correctly [2]. Transportation is the most important area of logistics management due to its effects on customer satisfaction and cost structures of companies. If a company can effectively manage its traffic function, it will achieve significant improvements in its earnings and effectiveness [4]. Distribution, in its lean form, can be described as one of the most important parts of the value creation process for customers. The scope and wealth of this value offered parallels the way companies make the necessary organizational arrangements in line with their ability to deliver the products they have produced to their customers. Failure to create the value expected by customers is related to failure to develop strong distribution channels that can meet expectations [24].

LOGISTICS COSTS Logistics costs can be defined as the cost to the beneficiaries of the logistics services or as the cost to purchase. The market price of logistics transactions is determined not only based on supply and demand, the scope, quality and quality of services provided, but also on factors such as international trade volume, economic development and international competition [2]. Today, the importance of logistics function in the cost accounting systems of enterprises is increasing. Management of logistics costs for

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businesses has become very important in product profitability, pricing decisions, customer profitability and operating profitability [21]. Logistics costs, which cover all costs caused by material flow and related information flow within a supply chain, account for a significant percentage of sales today [25]. Reasons for increasing logistics costs in the research conducted by Europen Logistics Association and Consultancy [25];   

Increased global procurement, Increased complexity in enterprises with increased product differentiation and adaptation of products to customers, It is determined that customers demand more value-added service levels from suppliers.

Accounting and control of logistics costs requires some adjustments to the input chain within the company as well. This can be accomplished with integrated logistics management and requires the identification, measurement and comparison of many factors. The factors that should be determined can be listed as follows [22]:      

Define the structural determinants and behavior of the enterprise’s cost drivers or logistics activities; Measure in sufficient detail to determine the causes and consequences of cost drivers; Measuring the interaction of cost drivers; Determine and measure certain levels of service that are important to customers; To see the links between logistics and service criteria; To evaluate them individually and together.

All relevant cost elements, from the acquisition of a system to the removal of inventory, are:

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Yavuz Ozdemir 1. Transportation Costs: In the 1960s, traditional distribution methods began to become expensive and managers felt the need to control these costs. Transportation costs became even more critical in the 1970s with rising oil prices, and the need to solve the problem grew considerably. Transport, which in the past was a fixed expense item for planners, has now been a variable and must be considered. The costs associated with transport costs can be divided into elements as follows: 2. This elaboration is necessary to distinguish the costs from other costs, which may result in an increase in the total cost of changes in the logistics system. If the transport costs include other subgroups, these groups should be added to the list and evaluated. For ordinary carriers, the associated costs can be found by calculating the costs of sample product flows or by examining the transport bills. For private transport fleets, accounting records can be used.  Moving towards the company  Transports outside the company  Transports to the seller  Transports to the customer  Transports to the carrier  Product-related transports  Transports on the distribution channel. 3. Storage Costs: Storage costs are expenses that can be increased or eliminated depending on changes in storage activities. Storage costs are usually fixed costs and they feature a step function. In order to eliminate this fixed cost, the warehouse must be closed. Labor-related costs have both fixed and variable cost components. For example, in order to operate a warehouse, employees such as warehouse chief, office worker, protection officer are needed. However, the concentration of goods in and out of the warehouse may require these employees to work overtime. This is a variable cost. Storage costs should therefore be divided into two separate categories:

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 Total flow-related costs  Costs related to storage The costs associated with total flow are the costs incurred in meeting the demands of a market by moving goods into and out of the warehouse in that market, and the fixed costs associated with these activities. The costs associated with inventory storage should be evaluated within the inventory handling costs. These costs vary according to the level of inventory within the warehouse. These two costs, which are different from each other, must be dealt with separately in order to go to accurate cost analyses. 4. Order Processing and Information Costs: Order transmission, order entry and processing, related transport costs and internal/external communication costs are examined under this group. While management focuses on these costs, it should be considered the importance of the information obtained by bearing the costs in the decision-making process. 5. Costs Depending on Lot Size: It is the costs that depend on production or purchase/supply costs that will change as a result of changes in the logistics system. The costs associated with batch size in production are: preparation time, control, discards during preparation, inability to be efficient due to the start of operation. Most firms use production preparation and capacity loss costs as data in production planning. Other costs can be calculated by their reaction to the change in party volume. The data obtained in this way can be used in the planning of the logistics system. 6. Inventory Handling Costs: Calculating the costs of moving inventory is as difficult as calculating the costs of lost sales. In this cost group, only costs that vary depending on changes in inventory level should be considered. a. Capital costs; opportunity costs of other activities that the company does not manage with the money tied to the inventory. b. Inventory service costs; tax and insurance costs on inventory.

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Yavuz Ozdemir c. Landfill costs; rent and other expenses of the warehouse location. d. Inventory risk costs; costs of damage, resettlement, and theft risk.

Determining these costs helps decision makers both in the process of establishing the logistics system and in the process of making changes to the existing system.

HAZARDOUS MATERIALS LOGISTICS Hazardous materials are substances that may endanger general safety and order, Society, Life Resources, animals and plants in connection with the transport process due to their nature, characteristics and circumstances [26]. Mistakes made during the transportation of hazardous materials, disruptions, causes such as cost concerns lead to irreversible consequences. For this reason, the transportation of hazardous materials requires special care and attention. For this, transport is a process that requires attention and involves many responsibilities. Especially the environment and human health should be considered and acted upon during the loading, unloading and distribution of hazardous materials. It must be transported, stored and distributed with systems and vehicles in accordance with the regulations. There are also many conditions during these processes. One of these is the necessity of packaging dangerous substances. In accordance with the provisions of the “Regulation on the Transport of Hazardous Materials”, substances that may leak, spill, contamine etc., needs to be packaged in spite of such problems. The UN number corresponding to each hazardous materials must be placed on the front of the package. However, it is important to be able to read and see the packaging signs easily, not to be damaged in open weather conditions, and to include the word “salvage” if it is scrap [27]. Labelling

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is another matter to be considered when transporting hazardous materials. When placing the label, the packaging should be placed so as not to prevent any parts from appearing. In addition, if there are more than one label, each should be placed so that it appears [27]. Labeling of vehicles is another issue. When labeling vehicles, they must be labeled as visible from both sides of the vehicle and from the rear. The state in which the product will be transported (solid, liquid, gas) is important in the transportation of hazardous materials by road. Vehicles to be used are determined according to the properties of the items to be transported. At the same time, the bulk or packaged transport of hazardous materials is also effective in determining the type of vehicle. 

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Open Vehicles: Vehicles with at least one side open. In order to transport hazardous materials in such vehicles, the substance must have its own protection cabin or the hazardous materials must not be affected by climatic conditions. Otherwise, hazardous materials are not allowed to be transported by these vehicles. Awning Vehicles: These types of vehicles carry packaged hazardous materials. In order to carry out the transport, there is no water or rain leakage to the load-carrying part of the vehicle and the inside of the vehicle is not required to be affected by physical factors. Heat-controlled Vehicles: The reason why heat-controlled vehicles are used in the transportation of hazardous materials is that some hazardous materials can generate heat or burn themselves due to their heating during transportation. Therefore, it is necessary to fix the heat in the container and carry the substance with a constant temperature. At the same time, low levels of glare in some products may cause the hazardous material to explode or burn at high ambient temperature. Tankers: Tankers are used to transport flammable liquids. It can be single-section or in nine-section tankers. These vehicles carry thinner diesel fuel, gasoline and other flammable liquids [28].

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VEHICLE ROUTING PROBLEMS Vehicle Routing Problems (VRP) include assigning the most appropriate route to each vehicle to optimally serve customers whose capacity is determined by specific vehicles. Routes created in the solution of the VRP must in general provide the following characteristics:     

The route of each vehicle must start at the depot and end at the depot; All customer requirements (demands) must be met; Each customer should be visited once; Other system constraints must be provided; Total cost should be minimized.

VRP consists of many different components. The most important of these can be explained as the road network, customers, vehicles and warehouse(s) [14]. VRP basically has components of demand structure, material type, distribution/collection points and vehicle fleets. Demand structure, the demand whether it is dynamic or static; the type of material which type of material moving; distribution/collection points, location of warehouses of customers and determine whether it is known; vehicle fleet indicates whether the vehicles are homogeneous or heterogeneous [29]. Objective functions used in vehicle routing problems are as follows [30]:    

Minimize the fixed costs of the vehicles used and the transportation costs including the route or total time to travel; Minimize the number of vehicles required to serve all customers; Balancing routes in terms of vehicle load and travel time; Minimize penalty costs combined with customer services.

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The vehicle Routing Problem (VRP) is the problem of designing distribution and collection routes from a central warehouse to various geographically dispersed demand points to minimize the total distance traveled by the vehicle fleet [31]. In a standard VRP, the demands of customer points located at different points are tried to be met by means of the vehicles from the warehouses. In doing so, the goal is to effectively and efficiently determine the route that meets customer needs as soon as possible, by the shortest route and at the least cost. The following elements should be taken into account when the vehicle is rotating:     

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The demands of the customers in the network must be fully met; Each destination on the network must be visited only once by a single vehicle; The route must start at the depot and end at the depot again; The total demand of the customers on the route should not exceed the total capacity of the vehicle; Each vehicle must operate on only one route; The main purpose of vehicle routing should be to minimize the total distance the vehicles will travel [32].

The importance of VRP in transportation is better understood every day. Many major manufacturers, retailers and distribution companies generally have warehouses and fleets of vehicles established in various regions. Each warehouse is responsible for serving customers under certain boundaries in a particular region. The supply of products from a warehouse is provided by fleets of vehicles that vary in size and capacity. The main problem is to get variable quantities of materials and products to demand points as effectively as possible. In short, VRP are about finding the balance between service and cost. In addition, many constraints are taken into consideration according to the content of the problem [33]. The main constraints encountered in the applications of VRP are [34]: Restrictions on vehicles:

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Yavuz Ozdemir        

Capacity Working Hours Costing Number of laps Type of vehicle to be used The nature of the demand The case of dividing the product into more than one vehicle In case of more than one warehouse

In addition, routing problems are defined as finding minimum cost routes that meet customer demands under various constraints. However, determining the purpose in an important issue in VRP. Some of the objectives that can be chosen as targets in a VRP are as follows [33]:    

Maximizing the duration of use of the vehicles; Maximizing the capacity utilization rate of vehicles; Minimizing the distance of travel; Minimizing the number of vehicles used.

Vehicle Routing Problem is a widely known integer programming problem that belongs to NP-Hard problems where the computational power required to solve the problem increases exponentially with the size of the problem. In order to reach the right result quickly in such problems, it is aimed to find approximate results. This task is often accomplished by various heuristic methods that try to understand the nature of the problem. Vehicle Routing Problem is the transportation of goods through vehicles on a road network. From this perspective, some components of this problem can be mentioned in real life. These components are given below [35]: 

Road network: In the VRP, the road network is indicated by a series. There are knots and separations in the series. In VRP, the paths correspond to the separations in the array and the stops

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(customers) correspond to the nodes in the array. The series representing the road network may consist of directional, nondirectional, or a mixture of both directional and non-directional separations; Customers: In VRP, customers are units waiting for service, i.e., demanding a certain amount of goods from the warehouse or supplying a certain amount of goods to the warehouse. In a series, customers are represented by nodes; Vehicles: Each vehicle has a carrying capacity. Carrying capacity can be in weight or in volume. In addition, the carrying capacity of each vehicle may be the same or in some problems may be used in vehicles with different carrying capacity; Warehouses: Warehouses in VRP are units where various or similar types of vehicles are located and form the center of the distribution plan. Decisions to be made and plans to be made are based on the idea of which points the vehicles should leave the warehouse, and stop and return back to the warehouse. There may be only one warehouse or, in some problems, there may be more than one warehouse; Drivers: Drivers must be considered indirectly, even if they are not directly taken into account in vehicle routing problems. In real life applications, trade union and contract terms should be reflected in the models. Since the laws specify the working periods, shifts, overtime conditions and the rest breaks that drivers must give, the distribution plans created must be made according to these regulations; Operational constraints: Constraints in VRP vary depending on the nature and requirements of the transport carried out, the quality of the transport service provided and the working contracts of the drivers. In general, however, constraints in a VRP are summed in two classes. The first is the local constraints and the constraints that apply to a single round. The second is the holistic constraints and the constraints that apply to all rounds.

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

It is ensured that the number of rounds is as much as the number of vehicles, if given, the maximum number of laps for the vehicle or warehouse is not suspended, the load balancing between the drivers, the working periods and shifts are arranged so that there is a certain minimum time interval between the laps [36]; Objectives: As with every optimization problem in the Operations Research, VRP attempts to optimize many different objective functions. Examples of some of these objectives are:  Minimizing the total of transportation costs and fixed costs of vehicles used in transportation,  Minimizing the number of vehicles and/or drivers,  Balancing tour times, distances, costs,  Minimizing the total penalty to be incurred for customers who are not fully or partially serviced,  Minimizing total distance,  Minimizing total time.

In VRP, one of the above objective functions can be studied for optimality, as well as several objective functions that are contradictory to each other can be studied for optimality. In this case, different methods of solving multi-objective decision making problems can be used for VRP [37].

APPLICATION In application, dataset from a fuel company is used. The mathematical model is established to determine the most appropriate route for this fuel company from its warehouse in Yarimca to 15 stations in Istanbul in order to meet the demands with 10 vehicles of heterogeneous fleet type. Also, an optimization software is used to solve it. The distances of 15 stations to each other and to the warehouse in Yarimca are shown in Table 1.

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Table 1. Distances of stations to each other and to the warehouse

Table 2. Vehicle capacities (kg) Vehicle 1 2 3 4 5 6 7 8 9 10 Total

Capacity (kg) 1,500 2,000 2,000 2,000 2,000 2,500 2,500 3,000 4,000 5,000 26,500

10 vehicles will be used for fuel transportation to these stations. The capacities of these vehicles are shown in Table 2. There is a cost of gasoline per km depending on the capacity of each vehicle. These costs are shown in Table 3. In other words, these costs are transportation costs.

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Yavuz Ozdemir Table 3. Gasoline costs of vehicles per km

Vehicle 1 2 3 4 5 6 7 8 9 10

Cost 0.20 0.30 0.30 0.30 0.30 0.40 0.40 0.60 0.70 1.00

Table 4. Station demands (kg) Stations Beykoz Nuhkuyusu Ziverbey Merdivenkoy Istiklal Kayisdagi Imes Sile yolu Kartal Soganlik Kurtulus Ptersane Kurtkoy Orhanli Formula Total

Demand (kg) 1,000 1,234 1,567 1,890 1,012 2,000 2,100 1,980 1,760 1,453 1,299 1,789 2,957 2,222 906 25,169

Station demands are intuitively determined by the level of development of the area where the stations are located. These values are hypothesized, and shown in Table 4.

VEHICLE ROUTING MODEL The aim of the vehicle routing model is to find the most appropriate route that minimizes the cost of transportation depending on distances. The following model has been established to find the optimal route:

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Indices: i: j: k:

Station point (i∈A) Station point (j∈A) Number of vehicles (k∈K)

Decision Variables: xkij: yij:

1, if the k vehicle goes from i to j; 0, otherwise. 1, if a vehicle goes from i to j; 0, otherwise.

Parameters: N: di: uij: cijk: Qk: mk: Q:

Number of vehicles Demand of the ith station Distance from ith station to jth station Cost of transportation of vehicle k vehicle from i to j Vehicle capacity Cost of gasoline per km Node set

Mathematical Model: 𝑘 Min. Z = ∑𝐾 𝑘=1 ∑𝑖,𝑗∈𝐴 𝑐𝑖𝑗𝑘 𝑥 𝑖𝑗

(1)

with respect to 𝑘 ∑𝐾 1 𝑥 𝑖𝑗 = 𝑦𝑖𝑗

(2)

∑1≤𝑗≤𝑛 𝑦𝑖𝑗 = 1 ∀𝑖 = 2,3, … . . 𝑛

(3)

∑1≤𝑖≤𝑛 𝑦𝑖𝑗 = 1 ∀𝑗 = 2,3, … … 𝑛

(4)

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Yavuz Ozdemir ∑1≤𝑗≤𝑛 𝑦1𝑗 = 𝐾

(5)

∑1≤𝑖≤𝑛 𝑦𝑖1 = 𝐾

(6)

∑2≤𝑖≤𝑛 ∑1≤𝑗≤𝑛 𝑑𝑖 𝑥 𝑘 𝑖𝑗 ≤ 𝑢 ∀𝑘 = 1,2, … … 𝐾

(7)

∑𝑖∈𝑄 𝑦𝑖𝑗 ≤ |𝑄| − 1 ∀𝑄 𝑓𝑜𝑟 𝑠𝑢𝑏𝑠𝑒𝑡 {2,3, … … … 𝑛}

(8)

𝑦𝑖𝑗 = {0,1} ∀(𝑖, 𝑗) ∈ 𝐴 )

(9)

𝑥 𝑘 𝑖𝑗 = {0,1} ∀(𝑖, 𝑗) ∈ 𝐴 𝑘 = 1,2, … … . . 𝐾

(10)

The objective function includes a minimum cost route, taking into account the costs of each vehicle going from i to j. The cost is written as cijk, as it was made with heterogeneous fleet-type vehicles. In constraint 2; if (i, j) is used, a vehicle is assigned. In constraint 3 and 4, it is stated to enter and exit each station once. i and j are also started from 2. Because the warehouse is assigned to 1. Constraint 5 and 6 specify k times to enter and exit to/from the warehouse, once for each vehicle. Constraint 7 ensures that the demand of the stations does not exceed the vehicle k capacity. In constraint 8, sub-rounds that do not involve the warehouse are prevented. The warehouse will enter exactly the k subround in every possible solution. Constraint 9 and 10 show that variables can take a value of 0 or 1.

Assumptions  

Return to the warehouse: Each vehicle must return to the warehouse after completing its service. Costs: Only transportation costs were considered. The cost of transportation is also calculated based on the multiplication of the

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 

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cost of gasoline per km of each vehicle, depending on the distances and the capacity of the vehicle. Station demands: Due to the confidential station demands, hypothetical values are taken for the model. The hypothetic values are calculated in direct proportion to the level of development of the region. Using all vehicles: The model was established assuming that all vehicles in the warehouse will participate for the VRP. Showing station names numerically: Since station and warehouse names will not be written for the model, numbers are assigned to each station in order to say 1 to the warehouse. Thus the model became easier to solve and understand. Table 5. Vehicles and optimal routes Vehicle

Route 1-3 1-4 1-6 1-7 1-8 1-9 1-10 1-11 1-14 1-15 2-1 3-2 4-1 5-1 6-12 7-1 8-5 9-1 10-1 11-1 12-1 13-1 14-1

1

2

3

4

5

6

7

8

9

10

X X X X X X X X X X X X X X X X X X X X X X X

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For solving the model with the dataset, firstly the indices are defined, destinations are indicated, and the number of vehicles are shown. Afterwards the distances between stations, vehicle capacities, vehicle costs, and station demands (Tables 1-4), objective function and constraints are defined to the optimization software. The optimal route is found by solving the model in the software and these routes are shown in Tables 5. According to Table 5, ten routes are formed for ten vehicles. At the end of each route, the vehicle must have returned to the warehouse. Routes for each vehicle is shown in Table 6. Table 6. Routes for each vehicle Vehicles 1 2 3 4 5 6 7 8 9 10

Routes 1-11-1 1-7-1 1-4-1 1-10-1 1-9-1 1-6-12-1 1-3-2-1 1-14-1 1-8-5-1 1-16-15-13-1

As a result, the minimum cost is calculated as 602,670.

CONCLUSION In order to survive in a rapidly changing and evolving competitive environment and to compete with competitors and expand their market share, enterprises must minimize their logistics and distribution costs, which is a significant cost for them. This can be accomplished with a feasible and optimal distribution plan. The biggest constraint in the provision of this plan is the capacity to meet the demands. At the same

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time, a minimum cost distribution plan must be created while the customers’ demands are fully fulfilled. For this reason vehicle routing techniques play an important role. Meeting the demands at the shortest distance and time, reduces the costs of collecting and distributing, also increases customer satisfaction and ensures more sales. In addition, the company’s service quality increases and it is ensured that it has an important position in the market. In this study, the problem of LPG distribution by heterogeneous fleettype vehicles from a fuel company’s warehouse to 15 stations in Istanbul province and the return of empty vehicles to the warehouse was discussed. The type of problem being studied is the distance and capacity constrained VRP. After the application was defined the model was solved using a optimization software. As a result, there were 10 heterogeneous fleet-type vehicles and 10 routes. Because it was assumed that all the vehicles had to be used in the model and the model was written accordingly. In this way, the total cost was 602,670. The total demand was 25,169 kg and the total vehicle capacity was 26,500 kg. Accordingly, the average capacity utilization rates of the vehicles were approximately 94.98%. This indicates that the results are in good value in terms of transportation costs and utilization rates. Thanks to this route, a good efficiency has been obtained from existing vehicles. But it should be remembered that changing economic conditions and demands can lead us to different paths. In this respect, change should not be resisted, should be pioneered.

REFERENCES [1] [2]

Demir, Volkan. “Logistics Activities and Costs”, Mali Cozum Journal, ISMMMO, Ofset Publishing, 74, (2006): 116. Koban, Emine and Yildirir Keser, Hilal. “Logistics in Foreign Trade”, Ekin Publishing, (2007):43.

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[4] [5] [6] [7] [8] [9] [10] [11]

[12]

[13] [14]

[15] [16]

Yavuz Ozdemir Canitez, Murat and Guclu, Tumer. Logistics in Export and Import, Applied Export-Import and Documentation, Gazi Bookstore, Ankara, (2005):153. Lambert, Douglas M. and Stock, James R. Strategic Logistics Management, Boston, Irwin/Mc Graw-Hill Edition, (1999):31. IGEME. Practical Information in Export, Lojistik Publication, (December 2004):8. Birdogan, Baki. Logistics Management and Logistics Sector Analysis, Lega Bookstore, Trabzon, (2004):15. Tek, Omer Baybars. Principles of Marketing - Global Managerial Approach Turkey Applications, İstanbul: Beta Publishing, (1999). www.btinsan.com, accessed May 1, 2018. Gulenc, Figen and Karagoz, Bihter. Logistics in Export and Import, Applied Export-Import, Istanbul, (2008). Waters, Donald. Global Logistics and Distribution Planning, Kogan Page Limited, London, U.K, (2003). Dantzig, George Bernard and Ramser, Joseph. An Ant Colony System Hybridized with a New Local Search for the Sequential Ordering Problem, (2000). Vural, Erol. Design and Implementation of a Population and Neighborhood Based Meta-heuristic Algorithm for Vehicle Routing Problems, Master Thesis, YTU Graduate School of Natural and Applied Sciences, Istanbul, (2006). Alkan, Tugce. Vehicle Routing and Meta-Intuitive Logic, Master Thesis, Istanbul University, Istanbul, (2003). Yurtkuran, Alkin. A New Meta-Heuristic Approach for the Solution of Vehicle Routing Problems: Electromagnetic Algorithm, Master Thesis, Uludag University Graduate School of Natural and Applied Sciences, Bursa, (2009). Ozkan, Pinar. Vehicle Routing and Scheduling, Master Thesis, YTU Graduate School of Natural and Applied Sciences, Istanbul, (2006). Derya, Kasim. Solving Vehicle Routing Problem with an Intuitive Approach, Istanbul, (2008):3.

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[17] Kobu, Bulent. Production management, Istanbul, Avcıol Publishing, (2003). [18] E-Logistics and Logistics Information Systems (2005). http:// www.meslekiyeterlilik.com/lojistik/1.E-Lojistik.pdf, accessed May 6 2018. [19] ITO. Turkey Logistics Infrastructure Sector Analysis, Istanbul, ITO Publication, (2006):14. [20] Tanyas, Mehmet. Importance of inventory management in logistics http://www.ekol.us/zirve/2004/15.html, accessed June 2018, (2004). [21] Keskin, M. Hakan. Logistics Supply Chain Management: Past, Change, Present, Future, Ankara: Nobel Publication Distribution, (2006). [22] Demir, Volkan. Cost Estimation in Logistics Management System, Ankara: Nobel Publication Distribution, (2007). [23] Filiz, Atilla. Logistics and Inventory Management, http://www. bilgiyonetimi.org/cm/pages/mkl_gos.php?nt=549, accessed June 2018, (2004). [24] Erol, Ismail. Distribution Networks in Traditional and Electronic Supply Chains, Alternative Designs and Decision Making, Pazarlama Dünyası Dergisi, sayı 5, s. 48-51, http://www.pazarla madunyasi.com.tr/dergioku.php?haberid=17, accessed May 2018, Pazarlama Dunyasi Journal (2004):5. [25] Erdogan, Nurten. Logistics Costing and Activity Based Costing in Logistics, Eskisehir, Anadolu University Faculty of Economics and Administrative Sciences Publishing, (2007):22. [26] United Nations Economic Commission for Europe. European Convention on the Carriage of Dangerous Goods by Road, Cenevre, (2007). [27] T.C. Official newspaper. Implementing Regulation on the Carriage of Dangerous Goods by Road, (2007). [28] Erdal, Murat, Gorcun, Omer Faruk, Gorcun, Ozhan and Saygili, Mehmet Sitki. Integrated Logistics Management, Beta Publishing, (2008):337-339.

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[29] Seker, Sulbihan. Vehicle Routing Problems and Genetic Algorithm Approach to Stochastic Vehicle Routing Problem with Time Window, Master Thesis, YTU Graduate School of Natural and Applied Sciences, Istanbul, (2007). [30] Toth, Paolo and Vigo, Daniele. The Vehicle Routing Problem, Philadelphia: SIAM, (2002):1. [31] Eryavuz, Mert and Gencer, Can. An Application of Vehicle Routing Problem Suleyman Demirel University Faculty of Economics and Administrative Sciences, (2001). [32] Tan, Keah Choon. A Framework of Supply Chain Management Literature, Europan Journal of Purchasing Supply Management, (2000). [33] Rushton, Alan and Oxley, John. Handbok of Logistics and Distributiion Management, Kogan Page, London, (1991). [34] Ghiani, Gianpaolo, Guerriero, Francesca, Laporte, Gilbert and Musmanno, Roberto. RealTime Vehicle Routing: Solutions Concepts, Algorithms And Parallel Computing Strategies, European Journal Of Operational Research, Article In Pres, (2003). [35] Paolucci, Massimo. Vehicle Routing Problems, ICCS, (2005):70. [36] Murata, Tadahiko and Itai, Ryota. Multiobjective Vehicle Routing Problem Using Two-Fold EMO Algortihms to Enhance Solution Similarity on Non-Dominated Solutions, https://www.semantic scholar.org/paper/, accessed May 8 2018, (2008). [37] Calvete, Herminia I., Gale, Carmen, Oliveros, Maria Jose and Sanchez Valverde, Belen, A Goal Programming Approach to Vehicle Routing Problems with Soft Time Windows, European Journal of Operational Research, 177(3), (2007):1720–1733.

In: Heating Systems Editor: Elias Moore

ISBN: 978-1-53617-557-8 © 2020 Nova Science Publishers, Inc.

Chapter 4

SKIN-SYSTEMS FOR HEATING EXTRA-LONG PIPELINES Michail Strupinskiy, PhD and Nikolay Khrenkov*, PhD “Special Systems and Technologies” Company Group, Mytischi, Russia

ABSTRACT Three types of electro heating skin-systems are presented in the article. The main features of skin heating systems are considered. The advantages of these systems for heating extra-long pipelines transporting oil, gas, water and other liquids are shown. Keywords: electro heating skin-systems, extra-long pipelines, oil, gas, water

INTRODUCTION In the process of developing oil and gas fields in the Arctic region, it is necessary to ensure stable and trouble-free operation of the transportation *

Corresponding Author’s E-mail: [email protected].

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systems of produced and process fluids: oil, gas, gas condensate, process water, as well as water and sewage pipelines of the settlements for personal, serving the field. In conditions when the warm season does not exceed 3 months, safe and reliable operation of pipelines is possible only if they are equipped with heating systems. As a result of a complex of scientific research and development works, several variants of heating systems for long and extra-long pipelines (up to 100 km and more) transporting gas, oil products, water and other liquids and operating in the climatic conditions of the Far North and Siberia have been created. Electric heating systems using heating cables have shown high efficiency and reliability and are widely used in oil and gas fields for heating on-site pipelines and tanks. In particular, heating systems “TEPLOMAG”, developed and mass produced by the group of companies “Special Systems and Technologies”, were recognized. On-site systems are characterized by a large branching, a variety of pipelines in size and length. At the same time, there is always an available source of electricity. The task of heating inter-site pipelines is decidedly different in that it is necessary to heat individual pipelines of great length, passing through an unpopulated area in which there are no sources of electricity. If you use traditional heating schemes based on self-regulating heating cables for onsite pipelines, the main problem becomes the problem of supplying power to the junction boxes, which should be placed along the pipeline every 100-200 meters. At the same time, the cost of the power supply system exceeds the cost of the heating cables themselves by 2-3 times. A generally accepted solution for heating extra-long pipelines is a closed inductive-resistive system using a skin effect or otherwise a system (SECT) [1]. Before the beginning of this work, the principle of inductiveresistive heating in Russia was known only in general terms, only as the principle. In the publicly available literature there were no publications, describing the engineering methods for calculating the characteristics of

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the skin system, such as linear power, supply voltage, operating current, depending on the size and properties of the components of the system and the length of the pipeline. In the course of this work, the idea of inductive-resistive heating of long pipelines was developed and the possibilities of using also open and flexible skin heaters were shown. Methods of calculation and modeling of electrical and thermal characteristics of all types of skin heating systems were developed, which allowed to design heating systems of long pipelines without intermediate power sources with high accuracy. Original designs of high-voltage cables, high-voltage connectors, power supply, junction and end boxes have been developed to ensure reliable operation of the system in harsh climatic conditions. For the operation of skin heating systems, non-standard voltage supply transformers are required, providing symmetrical connection of two-phase and single-phase loads to the threephase network. The winding circuits of such transformers were patented and their production was organized. The systems include a computerized heating process control and pipeline safety monitoring system that optimizes heating energy consumption depending on environmental conditions. Organized production of all the necessary components of the inductive-resistive heating systems. The problems of installation of heating systems at the facilities have also been successfully solved.

BRIEF DESCRIPTION OF SKIN SYSTEMS Our heating systems use inductive-resistive heaters of the following types: 1. Closed skin heaters in which the heating element consists of a special cable, placed in a continuous tube made of ferromagnetic steel, the wall thickness of which exceeds the 3rd penetration depth of the electromagnetic wave. Voltage is applied to the cable and tube.

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Michail Strupinskiy and Nikolay Khrenkov 2. Open skin heaters, in which the heating element is made in the form of a cable loop, along which individual tubes of ferromagnetic steel are placed with a small gap, the wall thickness of which exceeds the 3rd penetration depth of the electromagnetic wave. Voltage is applied only to the cable. 3. Flexible cable skin heaters of coaxial design, in which the inner conductor is made of well-conducting material, and the outer conductor is made of a composite and contains a layer of ferromagnetic material. Voltage is supplied to the inner and outer conductors.

SCHEMATIC DIAGRAM OF A CLOSED SKIN-HEATING SYSTEM The closed skin system, also called the SECT system, is widely used for heating long pipelines. This system contains heaters, consisting of a high-voltage cable of a sufficiently large cross section placed in a ferromagnetic (steel) tube (Figure 1). The supply voltage is supplied to the front end of such a system, and at the far end, the cable core and tube are connected to a short circuit.

Figure 1. Schematic diagram of a closed inductive-resistive heater: 1- cable core, 2 electrical insulation of cable, 3 – outer conductor - ferromagnetic tube, 4 - cable and tube end connection.

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Due to the current, flowing through the cable, thermal energy is released in the cable core according to the Joule – Lenz law. At the same time, the current, flowing through the cable, creates an electromagnetic field that interacts with the current, flowing in the tube in the opposite direction. The interaction of the electromagnetic fields of the inner and outer conductors gives rise to the following effects. The correct selection of parameters such as the current value, the value of the supply voltage and voltage drop per unit length, the cross section of the cable core and the wall thickness of the ferromagnetic tube, leads to the fact that even at a frequency of 50 Hz, a full skin effect is realized. Due to this, in the steel tube current flows only near the inner surface (Figure 2). On the outer surface of the tube, the electric potential is practically absent. Since the thickness of the skin layer is small, its resistance significantly exceeds the resistance of the inner conductor, and up to 80% of the total heat flux is realized in the steel tube. As follows from the above description and Figure 1, such a skin heater at the same time acts as a supply line, which allows heating of long pipelines, supplying power from only one point.

Figure 2. The structure of the electromagnetic field in a closed inductive-resistive heating system.

The need to use high-voltage cable and other high-voltage equipment is dictated by the need to heat long pipelines. The typical value of the linear voltage drop in the heater of this type is 0.25-0.40 V/m. If the entire

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system is designed for a supply voltage of 5,000 V, then it is possible to heat a pipeline with a length of up to 20 km from one point. When using a system designed for 10,000 V, the length of the heated pipeline can reach 40 km. Because the outer conductor of the skin system is a rather thick-walled steel tube, high mechanical strength and electrical insulation of the inner conductor is ensured. On the other hand, the zero potential on the outer surface of the heaters makes the closed skin system electrically safe. Therefore, a closed (classic) skin heater is laid directly on the surface of the heated pipeline and can be grounded at any point. This property is one of the conditions for the use of a closed skin heating system in hazardous areas.

SCHEMATIC DIAGRAM OF AN OPEN SKIN-HEATING SYSTEM In order to can be used an open skin system, also called induction, for heating a long pipeline with a power source at only one end of the trace; it must be made in the form of a loop (Figure 3). In this case, the individual steel tubes are not connected to each other electrically.

Figure 3. Diagram of an open skin-heating system with power supply from one end: 1ferromagnetic tubes, 2 - cable - inductor in electrical insulation.

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Figure 4. The structure of the electromagnetic field in an open (induction) skin heating system.

The field structure in the tubes of an open system differs significantly from what is observed in a closed skin system (Figure 4). The current, flowing through the conductor, and in this case causes induced currents in the tube, but they are closed in the body of the tube, flowing not only along the inner, but also along the outer surface of the tube. From the above it follows that constructive measures must be taken to prevent the possible consequences of the presence of electrical potential on the surface of the tubes of the induction heating system. A comparison of the specific parameters of the open and closed systems shows that the linear voltage drop per unit length of the heated pipeline in an open system is, on average, 1.8 times greater than in a closed system, and the current in the cable is 1.8 times less, respectively (Figure 5). The specific potential on the surface of the tube of an open system is 0.35–0.40 of the linear voltage drop across one line of the open system. In an open skin system, there is no need to ensure reliable electrical contact and tightness between the skin heater tubes, which is a mandatory requirement for a closed system. This feature allows you to simplify installation work when installing open system heaters and to abandon welding. For this reason, an open skin-heating system has the advantage when heating pipelines made of plastic pipes.

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Figure 5. Specific characteristics of closed (1) and open (2) skin systems. In a closed system there is one linear heater, and in an open system there is a heater in the form of a loop. In both cases, the power point at the beginning of the trace.

SCHEME OF A FLEXIBLE SKIN-HEATING SYSTEM In a flexible skin system, rigid steel tubes, produced with measuring lengths of 10–12 m, are not used, and the heater is made as a unit using cable technology. A composite outer conductor is superimposed on the reinforced insulated heating core. The first layer of the outer conductor is usually made of copper and the second layer in the form of a steel welded corrugated tube, tightly fitting the first layer. A polymeric sheath is applied over the outer conductor (Figure 6). This heater is connected to the power source in the same way as a closed skin heater (see Figure 1).

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Figure 6. The design of the flexible skin of the heater. 1- the inner conductor-inductor; 2 and 3 -layers of electrical insulation; 4 - the inner layer of the outer conductor; 5 - the outer layer of the inner conductor, made in the form of a corrugated steel sheath; 6 polymer protective sheath.

Figure 7. The dependence of specific power from the current three options of flexible skin-heater with different specific cross section (Sc) of copper part of outer conductor: 1) Sc = 1; 2) Sc = 1.5; 3) Sc = 2.

FEATURE OF SKIN SYSTEMS Electromagnetic energy from the source is distributed mainly along the conductors. Conductors are guides for the flow of energy, concentrating this flow in the immediate vicinity of themselves. Part of the energy gets from the surrounding space into the conductors through their surface. Inside the conductors, this part of the energy is absorbed and converted into heat, since the conductors have a finite electrical conductivity [2, 3]. As it spreads inside the conductor from layer to layer, the intensity of the alternating electric (and magnetic) field continuously decreases, therefore,

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the current density in the conductor, proportional to the intensity of the electric field, also decreases as it moves away from the surface of the conductor. The unevenness of the distribution of the density of alternating current across the cross-section of the conductor, with the concentration at its surface, was called the surface effect (skin effect). The unevenness of the current density distribution is greater the greater the current frequency, electrical conductivity and magnetic permeability of the conductor and the size of its cross-section. An indicator of the degree of manifestation of the surface effect is the “depth of penetration”. It is defined as the distance from the surface of the conductor at which the electric and magnetic field strength decreases by e times (e = 2.72). The current density decreases by the same number of times. The actual exponential decrease in current density as it moves away from the surface of the conductor can be replaced with sufficient accuracy by a uniform distribution of current density in the surface layer with a thickness equal to the penetration depth (Figure 8). It follows that the active resistance of the conductor when passing through it AC will be the same as the resistance of the same conductor when passing DC on its surface layer with a thickness equal to the depth of penetration.

Figure 8. Distribution of the current density in the conductor at the surface effect. The current density on the surface is taken as a unit.

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For the approximate analytical calculation of the power released in the skin system, we can use the formulas obtained for the idealized case of induction heating, i.e., the impact of a plane-parallel electromagnetic wave on a semi-infinite plane conducting metal plate with constant values of electrical resistivity and relative magnetic permeability (  and  ). The penetration of a plane electromagnetic wave characterized by vectors H and E, in an electrically conductive semi-infinite body with a flat surface is accompanied by the conversion of electromagnetic energy into heat, which leads to the weakening of electromagnetic fields (EMF) into the body, i.e., the decrease of the values of H and E for the exponential law (Figure 8). The greatest values of the field characteristics are on the surface of the body. These values are denoted by H0 and E0. To assess the process of penetration of the electromagnetic wave, as mentioned above, the concept of penetration depth Δ of the electromagnetic wave (m) is introduced:

∆=√

2𝜌 𝜇0 𝜇𝜔

(1)

where: ρ - electrical resistivity, Ohm·m; μ - relative magnetic permeability of the material; μ0 = 1.256·10-6 - magnetic constant; ω - circular frequency of EMF; The parameter Δ is introduced for the case of propagation of a plane electromagnetic wave in a plane semi-infinite body with constant electro physical properties: ρ and μ. The penetration depth Δ is included in the formulas for the distribution of the magnetic H and electric E field strengths and, as well as the current density J, which at depth Δ decrease by e = 2.72 times compared to their values on the surface of the body. Accordingly, the active power P (the active component of the total power S) released in the semi-infinite body decreases in proportion to the product H and E, i.e., the square of the exponent, and at a depth Δ decreases by e2

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times, i.e., in a layer equal Δ to almost 86% of the total power of the electromagnetic wave converted into heat is released. Due to the variability of the EMF parameters in the depth of the real conductive body, the power released in it, i.e., the energy released per unit time, is conveniently characterized by the specific surface power

pos

2

assigned to the unit of the energy-receiving surface (W / m ), which is determined by the formula: 𝑝𝑜𝑠 = 2 ∙ 10−3 𝐻02 √𝜌𝜇𝑓

(2)

where: H0 - the effective value of the magnetic field strength, A / m Approximately, it is possible to consider as a semi-infinite body the tube at which internal diameter d and thickness of a wall δ satisfy conditions: 𝑑 ≥ 10∆ and 𝛿 ≥ 3∆

(3)

Industrial electric heating devices are characterized by the use of relatively weak electromagnetic fields (H < 2000 A / m). The penetration depth at 50 Hz for commonly used ferromagnetic structural steel is approximately Δ = 1 mm. Typically, tubes with a wall thickness of at least 3 mm are used, so the condition of complete attenuation of EMF is observed and it is permissible to apply approaches valid for semi-infinite plates in the calculations. If the wall thickness is small, and the curvature of the surface is significant, then these differences must be taken into account in calculations using accurate formulas or modeling methods. The approaches used in the development of methods for calculating the parameters of skin heating systems are described in [4, 5].

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RELATIONSHIP OF SPECIFIC POWER AND LENGTH OF HEATING SYSTEM When calculating the power characteristics of skin heating systems, two limiting factors should be taken into account. With a relatively short length of the system, the limit of the specific linear power is associated with the maximum permissible temperature to which the cable can be heated, and ultimately, with the maximum permissible current, the value of which is limited by the cross section of the cable core. For long system lengths, the limiting factor is the operating voltage for which the inductor cable insulation is designed. Below are the results of calculations for a number of typical versions of skin heating systems, which can be used to assess the possibility of heating a particular pipeline. The calculated dependences between the linear power, supply voltage and length of the heated pipeline are shown in Figure 9a. Typical variants of the use of cables with polyethylene cross-linked insulation with a maximum heat resistance of 80-90°C and cores with a cross section of 10 and 20 mm2 are considered. As the outer conductors is provided the use of steel tubes of 10 or 20 steel, of an external diameter of 32 mm and a wall thickness of 3 mm. Insulation of the cable section 10 mm2 designed for a maximum supply voltage of 2000 V and cable cross section of 20 mm2, for a voltage of 4000 V. The calculations are performed for extreme cases when oil is pumped through the transport pipe with a temperature of 40°C, and the cable insulation is heated to a temperature not higher than 80°C. The graphs have two characteristic areas: in the initial part, there is no dependence of power on length, since here the limiting factor is the maximum permissible temperature (80°C) of the insulation of the cable. Corresponding to these conditions, the maximum current in the core is 120 A for the cable with a cross section of 10 mm2 and 170 A for the cable with a cross section of 20 mm2. In the initial zone (at relatively small lengths), the supply voltage is less than the maximum permissible. The dependence of the supply voltage on the length of the route is shown in Figure 9b.

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a)

b)

Figure 9. The dependence of the characteristics of skin heating systems on the length: a) - specific power due to the cross-section of the core and the supply voltage; b) - the dependence of the supply voltage on the length of the system.

After the value of the supply voltage is compared with the limit, we move to the second zone, in which there is a significant dependence of the linear power on the length of the pipeline. In the entire second zone, the supply voltage is constant and equal to the maximum permissible. It is the magnitude of the supply voltage that limits the maximum possible linear power of the system. The inflection point for a cable with a cross section of core10 mm2, with Umax = 2000 V, falls on a length of 3160 m, and for a cable with a cross section of core 20 mm2, with Umax = 4000 V, falls on a length of 5900 m. These lengths show the limit distances within which it is possible to use these heating systems with maximum power. At longer lengths, systems can also operate, but with less linear power, as shown in Figure 9a. In practice, quite often skin systems work with a level of linear power of 30 W/m. With this level of power skin system with a cable of 10 mm2 and a limit voltage of 2000 V provides heating of pipelines up to 4800 m, and the system with a cable of 20 mm2 and a limit voltage of 4000 V provides heating of pipelines up to 10200 m. The data presented in the graphs are valid for those cases when liquids with the specified temperatures circulate through the pipelines. However, during operation, long stoppages of the pumping process are possible. This mode should be considered separately, since in the absence of fluid flow, the heat transfer conditions deteriorate and overheating of the heating elements is possible.

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Figure 10. Appearance of high-voltage cables used in skin heating systems. 1 - cable for voltage 2 kV, core cross section 10 mm2, heat resistance 80°C; 2 - cable for voltage 3 kV, core cross section 20 mm2, heat resistance 80°C; 3 - cable for voltage 4 kV, core cross section 40 mm2, heat resistance 80°C; 4 - cable for voltage 2 kV, core cross section 10 mm2, heat resistance 180°C; 5 - cable for voltage 3 kV, core cross section 20 mm2, heat resistance 180°C.

The calculation of electrical characteristics is closely related to the thermal modes of the pipeline operation. The corresponding calculations in order to minimize the power of the heating system allowed to perform created in the course of this work, the software package “TeploMagPro” [6]. The complex “TeploMagPro” allows you to determine the value of heat losses when laying the pipeline above the ground (in the air), in the ground and in the water. The following can also be calculated: the cooling time of the pipeline to the critical temperature when the heating system is switched off and the heating time of the stopped pipeline. The influence of the heating system on the temperature of the flowing liquid can be determined depending on the flow rate, environmental conditions and properties of the liquid. A significant part of the research was related to ensuring the reliability and long service life of high-voltage skin cables. In closed and open skin systems, the structure of the electric field acting on the insulation of the skin cable is significantly different from the structure of the field of a conventional high-voltage power cable. There is an air gap between the skin cable insulation and the skin tube. It is dangerous because it can

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develop corona discharge, gradually leading to the destruction of electrical insulation. On the basis of these studies, recommendations on the design of electrical insulation of skin cables, on the use of corrugated protective hose and on ensuring the tightness of skin pipes and boxes have been developed. Special high-voltage cables of original design, normal and high heat resistance have been created. The design and manufacturing technology of skin cables resistant to installation and operation conditions have been worked out. Individual segments of skin cables are spliced using connectors placed in junction boxes (Figure 11).

Figure 11. Open junction box in which the skin cable connector is mounted.

TECHNICAL AND ECONOMIC EFFICIENCY OF INTRODUCTION OF SKIN-HEATING SYSTEMS OF EXTRA-LONG PIPELINES Skin heating systems allow solving the following tasks: 7. Ensure the safe operation of oil, gas condensate, water and aqueous solution pipelines throughout the ambient temperature

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9.

10.

11.

12.

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range, both during fluid pumping and at shutdown. At the same time, the risk from the impact of this object on the environment is reduced, working conditions are improved. With a pipeline length of 3 km or more, the skin system is the least expensive option of electric heating, since it does not require a special accompanying power supply along the pipe. In the long pipelines heated by skin systems, there is a significant amount of liquid. When forced to stop pumping and in the presence of electric heating is not required to drain the liquid from the pipe and have a special tank park for the drained liquid. In case of a power supply interruption, the skin system allows a long-time stop of oil and gas condensate pumping through pipelines. It allows completely painless to restore the operation of the pipeline, gradually heating the cooled pipe along the entire length, which is not realized in other methods of heating. Maintenance of the skin heating system does not require much labor, as the control system automatically maintains the specified mode of operation and, due to communication with the Central control room, provides constant monitoring of the system. When pumping viscous liquids such as oil and gas condensate, skin heating ensures optimum fluid viscosity at all temperatures. Consequently, there is no overload on the pump units and the associated overconsumption of electricity.

SYSTEM IMPLEMENTATION The use of the created heating systems ensured the normal year-round operation of pipelines for oil, gas and process water (well flooding), as well as pipelines for water supply to the settlements. The total length of pipelines equipped with skin-heating systems manufactured by the “Special Systems and Technologies” Group of Companies exceeds 600 km. Our first systems have been successfully operated for more than 15 years, in particular, at the fields of Gazprom, Total, Rosneft Oil Company,

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Michail Strupinskiy and Nikolay Khrenkov

LUKOIL Oil Company (Russia). Pipeline skin-heating systems installed at Kumho Mitsui Chemicals, Inc. (Republic of Korea) and the Vopak Horizon Fujairah Ltd (United Arab Emirates) oil terminal.

REFERENCES [1]

[2]

[3]

[4]

[5]

[6]

Masao Ando. 1962. Apparatus for maintaining liquid being transported in a pipe line at elevated temperature. Patent USA № 3293407, published 20.12.1966, declared 07.11.1963, priority from 17.11.1962. Demirchyan Kamo S., Neiman Leonid R., Korovkin Nikolay V., Chechurin Vladimir L. 2003 Theoretical foundations of electrical engineering. 4th ed. – SPb.: Peter. Strupinskiy Michail L., Khrenkov Nikolay N., Kuvaldin Aleksandr B. 2015. Design and operation of electric heating systems in the oil and gas industry: reference book. - Moscow: Infra-Engineering. Kuvaldin Aleksandr B., Strupinskiy Michail L., Khrenkov Nikolay N., Shatov Vitaly A. 2005 “Mathematical models for the study of the electromagnetic field in a ferromagnetic conductive medium taking into account hysteresis” Electricity № 11, pp. 54-61. Strupinskiy Michail L., Khrenkov Nikolay N., Kuvaldin Aleksandr B, Shatov Vitaly A. 2005 “Electrotermal model of coaxial inductiveresistive heating system” Russian Electrical Engineering” v.76, No 1, pp 51-56. Software package TeploMagPro version 6.0, 2018. Software package is developed by SSTEnergomontag.

INDEX A agricultural sector, 2 air quality, 57 algorithm, 45 alliance partners, 42 alternative energy, 39 ambient air, 10, 13, 15 ambient air temperature, 13, 15 architect, 39, 58

B bacteria, 19, 21, 38 benefits, 11, 17, 26, 41, 67 biomass, 9, 11 boilers, 3, 11, 24, 26, 37 business processes, 73 businesses, 75

C cables, 96, 97, 107, 109, 110 carbon, viii, 1, 11

changing environment, 69 circulation, 21, 38, 39 cities, viii, 1, 4, 11, 23, 28 climate, vii, 1, 3, 4, 28 climate change, viii, 2 communication, 71, 73, 77, 111 conductor, 98, 99, 100, 101, 102, 103, 104 construction, viii, 33, 36, 37, 41, 42, 43, 46, 58, 59, 61, 63 consumers, 23, 68 consumption, 10, 12, 17, 27, 37, 39, 66 cooking, 2, 37 cooling, vii, 1, 2, 3, 4, 5, 6, 7, 28, 36, 37, 109 coordination, 71 copper, 10, 102, 103 corona discharge, 110 cost, ix, 6, 8, 23, 43, 65, 66, 67, 69, 72, 74, 75, 76, 77, 78, 80, 81, 82, 85, 86, 88, 90, 91, 96 cost accounting, 74 cost structures, 74 customer relations, 61 customer service, 71, 72, 80

114

Index

customers, 23, 66, 67, 68, 70, 74, 75, 80, 81, 83, 84, 91

D decarbonisation, 2, 4, 7, 9 decision makers, 41, 78 decision-making process, 77 depth, 97, 98, 104, 105, 106 destruction, 37, 71, 110 developing countries, 36 diesel fuel, 79 disaster, 43 distribution, vii, ix, 6, 7, 8, 10, 13, 19, 20, 22, 65, 66, 68, 70, 71, 73, 74, 76, 78, 80, 81, 83, 90, 91, 104, 105 district heating, v, vii, viii, 1, 2, 3, 4, 5, 22, 28, 29, 30, 31, 32

E e-banking, 59 e-commerce, 45 economic competitiveness, 7 economic development, 74 electric field, 104, 109 electrical conductivity, 103, 104 electricity, viii, 2, 3, 4, 6, 10, 12, 13, 15, 16, 17, 25, 26, 96, 111 electro heating skin-systems, vii, ix, 95 electromagnetic, 97, 98, 99, 101, 105, 106, 112 electromagnetic fields, 99, 105, 106 energy, vii, viii, ix, 1, 2, 3, 4, 6, 7, 8, 14, 16, 17, 21, 22, 23, 25, 26, 28, 29, 30, 31, 32, 33, 34, 36, 37, 38, 39, 41, 57, 65, 97, 103, 105, 106 energy consumption, vii, viii, ix, 2, 3, 17, 26, 33, 34, 36, 37, 39, 97 energy efficiency, 2, 4, 7, 23, 36, 38 energy saving, viii, 33, 34, 37, 40

energy supply, 7 engineering, ix, 65, 96, 112 environment, 34, 38, 39, 43, 46, 59, 66, 72, 78, 90, 111 environmental conditions, 97, 109 environmental impact, 11, 60 environmental issues, 40 equipment, 22, 46, 62, 99 exergy, viii, 2, 8, 18, 26, 31 external environment, 57 extra-long pipelines, v, vii, ix, 95, 96, 110

F ferromagnetic, 97, 98, 99, 100, 106, 112 fixed costs, 76, 77, 80, 84 fuzzy AHP, vii, ix, 33, 34, 35, 36, 39, 40, 41, 42, 43, 44, 45, 47, 48, 49, 51, 52, 56, 57, 59, 60, 61, 62 fuzzy set theory, 43, 44 fuzzy sets, 42, 46

G gas, vii, viii, ix, 1, 3, 4, 8, 9, 11, 24, 38, 65, 66, 79, 95, 96, 110, 111, 112 gas distribution, 66 global trade, 67

H hazardous materials, 70, 78, 79 health, 19, 39, 72 heat loss, viii, 2, 8, 10, 13, 19, 22, 26, 109 heat pumps, 3, 6, 21 heat transfer, 21, 38, 108 heating systems, 1, iii, v, vii, viii, ix, 1, 4, 5, 6, 7, 8, 22, 24, 27, 32, 34, 35, 38, 39, 40, 51, 56, 57, 95, 96, 97, 106, 107, 108, 109, 110, 111, 112

Index housing, 23, 34, 36 housing warming, 34 human, 34, 36, 41, 78 human health, 78 humidity, 2, 36, 38, 57

I induction, 100, 101, 105 inductor, 100, 103, 107 industry, 46, 59, 63, 112 insulation, 8, 10, 19, 22, 37, 98, 100, 103, 107, 109 integration, 6, 7 interdependence, 46 international competition, 66, 74 International Energy Agency, 31 international trade, 67, 68, 74 issues, viii, 33, 45, 72, 73

L leakage, 11, 79 liquids, vii, ix, 79, 95, 96, 108, 111 logistics, v, vii, ix, 42, 59, 60, 65, 66, 67, 68, 70, 71, 72, 73, 74, 75, 76, 77, 78, 90, 91, 92, 93, 94 low temperatures, 39 low tempertaure, 2 LPG (liquefied petroleum gas), v, vii, ix, 65, 66, 70, 91

115

matrix, 43, 46, 47, 48, 50, 51, 52 methodology, 35, 36, 41, 42, 45, 46, 50, 52, 56, 57, 60, 62, 63

O oil, vii, viii, ix, 1, 3, 8, 24, 76, 95, 96, 107, 110, 111, 112 operations, vii, ix, 37, 65, 74 opportunities, 67 opportunity costs, 77 optimization, ix, 22, 37, 65, 66, 69, 70, 84, 90, 91

P parallel, viii, 33, 67, 105 performance indicator, ix, 65 petroleum, vii, ix, 65, 66 physical properties, 105 pipeline, 96, 97, 100, 101, 107, 108, 109, 111 preparation, iv, vii, ix, 65, 77 productivity growth, 67 profit margin, 72 profitability, 68, 75 programming, 70, 82 project, 6, 7, 22, 42, 43, 45, 46, 59, 60, 61, 63 protection, 37, 73, 74, 76, 79

R M magnetic field, 104, 106 management, 11, 25, 42, 60, 61, 66, 67, 68, 71, 72, 73, 74, 75, 77, 93 material handling, 46, 62, 71 materials, 37, 38, 67, 68, 71, 72, 78, 79, 81 mathematical programming, 68

raw materials, 66, 67, 73 renewable energy, 3, 4, 32 requirement, 26, 36, 67, 73, 101 resistance, 99, 104, 107, 109, 110 risk, 11, 21, 26, 42, 43, 61, 62, 63, 78, 111 risk assessment, 42, 43, 61, 62, 63 risk factors, 42, 43

116

Index

routes, 70, 80, 81, 82, 89, 90, 91

S safety, 37, 43, 78, 97 school, 5, 35, 41 scope, 68, 70, 71, 72, 74 service quality, 91 services, iv, 3, 37, 61, 66, 67, 68, 71, 72, 74 simulation, viii, 2, 4, 13, 14, 15, 16 skin, vii, ix, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111 society, 11, 34 software, ix, 45, 58, 65, 66, 70, 84, 90, 91, 109 solution, 69, 70, 80, 88, 96, 110 specific heat, 7, 13, 19, 21, 26 specific surface, 106 steel, 10, 38, 58, 97, 98, 99, 100, 102, 103, 106, 107 storage, vii, ix, 10, 11, 24, 25, 26, 31, 65, 66, 67, 68, 71, 73, 74, 76, 77 stoves, 34 strategic management, 66 structure, 37, 42, 73, 80, 99, 101, 109 suppliers, 62, 68, 75 supply chain, 60, 63, 66, 73, 75 support services, 72 surface area, 13 surface layer, 104 sustainability, 39, 43 sustainable energy, 8

T techniques, vii, ix, 33, 35, 43, 47, 57, 62, 73, 91

temperature, vii, viii, 1, 2, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 19, 20, 21, 22, 23, 24, 26, 27, 32, 36, 38, 39, 57, 79, 107, 109, 110, 112 thermal energy, 13, 25, 31, 99 thermal properties, 2 transport, 71, 72, 73, 76, 77, 78, 79, 83, 107 transportation, 4, 46, 62, 66, 67, 68, 71, 74, 78, 79, 80, 81, 82, 84, 85, 86, 87, 88, 91, 95

U urban areas, 7 urbanisation, vii, 1

V vehicle routing problem, ix, 66, 68, 69, 70, 80, 82, 83, 92, 94 vehicles, 69, 70, 73, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91 ventilation, 3, 36, 37, 38, 57

W waste heat, 3, 6, 9, 24 waste incinerator, 3 waste water, 3 water, vii, ix, 2, 3, 5, 6, 8, 10, 11, 12, 13, 19, 21, 22, 24, 25, 26, 38, 79, 95, 96, 109, 110, 111 workflow, 72 working conditions, 111 worldwide, viii, 6, 23, 33